{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data and Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 01 Importance of Data in Deep Learning - Fashion MNIST for AI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 02 Extract, Transform, Load (ETL) - Deep Learning Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "There are four general steps that we’ll be following as we move through this project:\n",
    "\n",
    "Prepare the data\n",
    "Build the model\n",
    "Train the model\n",
    "Analyze the model’s results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "The ETL Process\n",
    "In this post, we’ll kick things off by preparing the data. To prepare our data, we'll be following what is loosely known as an ETL process.\n",
    "\n",
    "Extract data from a data source.\n",
    "Transform data into a desirable format.\n",
    "Load data into a suitable structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to ./data/FashionMNIST\\FashionMNIST\\raw\\train-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.0%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/FashionMNIST\\FashionMNIST\\raw\\train-images-idx3-ubyte.gz to ./data/FashionMNIST\\FashionMNIST\\raw\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to ./data/FashionMNIST\\FashionMNIST\\raw\\train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "111.0%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/FashionMNIST\\FashionMNIST\\raw\\train-labels-idx1-ubyte.gz to ./data/FashionMNIST\\FashionMNIST\\raw\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to ./data/FashionMNIST\\FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100.0%"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/FashionMNIST\\FashionMNIST\\raw\\t10k-images-idx3-ubyte.gz to ./data/FashionMNIST\\FashionMNIST\\raw\n",
      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST\\FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "159.1%D:\\Anaconda\\envs\\pytorch\\lib\\site-packages\\torchvision\\datasets\\mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ..\\torch\\csrc\\utils\\tensor_numpy.cpp:141.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./data/FashionMNIST\\FashionMNIST\\raw\\t10k-labels-idx1-ubyte.gz to ./data/FashionMNIST\\FashionMNIST\\raw\n",
      "Processing...\n",
      "Done!\n"
     ]
    }
   ],
   "source": [
    "train_set = torchvision.datasets.FashionMNIST(\n",
    "    root = './data/FashionMNIST'\n",
    "    ,train = True\n",
    "    ,download = True\n",
    "    ,transform = transforms.Compose([\n",
    "        transforms.ToTensor()\n",
    "    ])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_loader = torch.utils.data.DataLoader(train_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quiz 02"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1:In machine learning and more generally in computing, the ETL process is a fractal     process related to data management. ETL in this context stands for _______________.\n",
    "A1:extract, transform and load\n",
    "\n",
    "Q2:In the context of the ETL data process, we _______________ data from a data source.\n",
    "A2:extract\n",
    "    \n",
    "Q3:In the context of the ETL data process, we _______________ data into a desirable     format.\n",
    "A3:transform\n",
    "\n",
    "Q4:In the context of the ETL data process, we _______________ data into a suitable     structure.\n",
    "A4:load\n",
    "    \n",
    "Q5:The ETL process can be thought of as a fractal process because it can be applied on     various\n",
    "A5:scales\n",
    "    \n",
    "Q6:To create a custom dataset using PyTorch, we extend the _______________ class by     creating a subclass that implements these required methods.\n",
    "A6:Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 03 PyTorch Datasets and DataLoaders - Training Set Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_set = torchvision.datasets.FashionMNIST(\n",
    "    root = './data/FashionMNIST',train = True,download = True\n",
    "    ,transform = transforms.Compose([transforms.ToTensor()])\n",
    ")\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "    train_set,batch_size = 10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "torch.set_printoptions(linewidth = 120)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\envs\\pytorch\\lib\\site-packages\\torchvision\\datasets\\mnist.py:45: UserWarning: train_labels has been renamed targets\n",
      "  warnings.warn(\"train_labels has been renamed targets\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([9, 0, 0,  ..., 3, 0, 5])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_set.train_labels.bincount()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = next(iter(train_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "image,label = sample# image = sample[0] label = sample[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 28, 28])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'shape' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-4cbaa5a81765>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'shape' is not defined"
     ]
    }
   ],
   "source": [
    "label,shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'int' object has no attribute 'shape'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-20-54bf38f54037>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlabel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'int' object has no attribute 'shape'"
     ]
    }
   ],
   "source": [
    "label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: 9\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(image.squeeze(),cmap='gray')\n",
    "print('label:',label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch = next(iter(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(batch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "images,labels = batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10, 1, 28, 28])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels tensor([9, 0, 0, 3, 0, 2, 7, 2, 5, 5])\n"
     ]
    },
    {
     "data": {
      "image/png": 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T4XAY169fV0QAN68nT54gl8shlUrh0qVLiEajKgTQ7XZjampKVTak3KCRC+wbijIXddThdrsRjUbh9/vx2muv4Zvf/Camp6dVuBm9bsw95VqzMthM00Q2m1XKrTTY5JzUizYBB+HQNILl/DcMQynm3W4Xk5OTeOutt1CpVHD37l2sra2hXC5jZWUF5XL5JYyig5PEoDDI+fl5/M2/+Tdx/vx5RKNRpFIpFZbLiAOuT5nGQMMfANrttpKHzLckMUMihnOR+wwrnNJge+utt3DlyhVUq1X8tb/211Cr1ZDP57GxsYFarYaPPvoIH3744XOhl4fl5I0brIzb04LL5VIe/E6ng93dXZRKJfX8fT4f6vW6ippw8Dzm5+fxC7/wC4jH43j8+DHu3buHer2OfD6PYrEI4IC4OO0IEcdgc9CnpJ0/fx7vvPOOMtio4FLISMFNT4YM2dG9HfREMFdDFnHQ89qsQA+F9La4XC5Eo1G4XC5kMhm43W6llDDc4ubNm2cwcscDFS5Zcr5Wq2Fzc1OF/pE5lx43KsJ8XqZpotFoKIaSHiHm8zHscXd3F/V6XSmD47LxMTyHVSBnZmZUXl6lUlEl03WWF+hXdqXiQM8cj5HH8u9er4dGo9EXniuNOY57vV6HYRiIRCKIRCIIBoNYWFjA9PQ03G43Pv3007MftGNC7w9Gj6FUxFKpFF577TW1htvtNkqlEm7duoXNzU1MT0/jnXfeQSqV6nseJBV0woDKIJW8cfH00CALhUKYmprCm2++ifPnz6Ner6Narar7pNIqZZ2cS3xfziUZEimP5Tnk8wiFQqpIE+WsfJ8eFQBIJBIqr40eUa/Xi42NjZc5lA7OAPF4HG+++SauXbumqgZTbtK7LSsSc3+XYeYyOoEtdzhPOS/pVSb5AkAVNnG73ZiZmYHH40Gn08Hly5fRbrextbWFR48eoVgsYm1t7dD0iXHZu+yg96nj36d1X2y/lEgk0Gq1UCgUAByErAcCAaVrOLBGPB7H66+/3td2yePxoFqtAkDfvnnakSKOwTYkuCEyv8PtdqPRaAyVMzXKgsblcmF6ehrnzp1DIpHA3NwcIpFIn3EFHAgYaaTZldnn/UohQGZOhvccZqzp4HfxeKmk9Ho9+Hw+JJNJ1Ot1xGIxRCIRS8/dqEAWCgEOxiibzeLJkycIh8OYm5tT4yk3Pyq7NNg6nQ7K5TKazSay2Sw2NjZUIQwq3zKEb1Tno0QoFML8/Dyi0aiqoGnlhZUCk2PFTUiSCK1Wq+99SSwA/WE/MgxXz8mQ4T4AlMeJ18Cqn+l0Wik61Wp1JOcgQQ8tFTFpLHCcmKMK9DOK6+vruHPnDmq1GhYXF5UnXs45es/kvKNiN27Mrt/vx/z8PCYmJjAzM6MUWla8tPOkWYWUAQeEGcF5opNaPFaGmUvvndX/PBevhT0fW60WvF4vHj9+rMquj/L8dHA0MEctlUrh6tWrmJycVGX35XqUc43zTcpUoN8goyFHrxplgq7j0BAEDuaebM3D7yHpEYlEcOXKFbz99tuoVCpjVRTrRXFSe7HMPaYM9nq9mJ6extTUFHq9HuLxuCJv6WFbW1tTIdKn7fEbJejELQlyptwwJeiNN97A4uIikskk8vk8crmcSrnRI2ickMgRgGTyY7EYZmdnEQgEsLW1hdXVVds8FSn4RnVDdLvduHbtGn7lV35FFcGYmJhQApYGqa7cArBU/iWDZOWZ06Gf147poaHH+Gvdo0JPRyKRgNfrxczMjKpaube3N7Dp58uCZMQBqMa5Dx8+RDabRSqV6iMKOp1OX7lw4MBgazab2N7eRqlUwtbWFj755BPkcjmVj8TKfOPU/yqdTuOLX/wiJiYm4Pf7lVdHjxuXXiHdIJPEQrVaVaGh9EZKZVmfx/pc1D0kfJ8eEq/Xq/rfTU5OqvDAnZ0drKysjHQOIQ3aWq2mPF5cc/QEMUSS+VE+nw/b29v4+OOP8a1vfQtvvfUWFhcXAUC1+3C73apsP8EQSM7dUSVU7BCJRPDOO+/gC1/4AmKxGNxuNyqVipoHel6Z7r3VDX7mruneNI67JCMYHs3/KQ+BA9lJA1iGCctcoqWlJSwuLuLhw4e4e/custlsXyicg/GD/twSiQS++c1v4r333kMmk8Ebb7yhcsYYckjDn5+Xe7EkZIB9Y4CVTK3mM3BANDAaBjgIsQT65yV/s/pvp9OB3+/HzMwMstks/v2///fY29v7XBkQL4pgMIj5+XmEQiHE43GkUinVF49hsLFYTBWCY+j1hx9+iLW1NTQajedC/V9VWKVQsIZDNBrFwsICfvVXfxVLS0tIp9O4cOEC/H6/0jHZL3N5eflM9y7HYBsCMhk/EAggGo0iGAyiWCwOdOEDh1dtetlgjPO5c+cQi8X68h5kWI8e5gg8L+R1SKVXsnl2Y3LYWOkeOT2vjSFAbKYcDAYVIziq0MP4ut0uKpWKquhYq9XQbDbVBqsbbDTWGAZZKpVQLBZVOWV6SxkOOC7eNQDKY5pOp/tC5uyuX843K2NL9ktjeA/DafV8Aoae6ueQPzJmXRIJzDcMh8OIRqNDyYmXDRmmJxPQpQHB8eN9MiyXeSizs7PKcAkGg31jJYsSSY/yOIVCEjTMZ2ZmVJVGegopL62et9W81cNprMgG3ejSQ4D188r/ZXEiXhfDybPZrGKUx0UmODgc9BTMzs7i8uXLiEajiMViCAQCaDQaSjEHrOek1dyVxJYkoZneAPQbbPq+BhwUJpORJazYa5omZmZmUK1WVR9MEkevEvTIIztYjZ983Wr9+3w+1Ss0mUwik8nA7/er3o1+vx9TU1OIx+PqHKZpIplMKrLzVYDd2NkdJ0kx7tvJZFJVRGe0DKMTMpkMAKjWFbpeeppwDLZnsHOP+nw+BINBzMzMKHYimUzC6/Wi2WyiUqkohZoKCBuayvMNM3EkBimlh51vGDAHIxQKIZFIIBqNIhQKAdj3GOheBP6WCoPsAcbf+v3I6zxsDPS+WdI70uv1lHdInkv/Tob9zM7O4rXXXlNJtqNYMZIsOo0zejDYwsAwDNy6dQsbGxtKkMhwBwDKa9RqtZDNZlEul1V4A/NhuDlaeY1GGdVqVZUl5wZEw5PzgF4rGl5UfGWTdqsQXq/XqzwVnHfSeLAybKVBJ73LktBhj7ZCoYDNzU1sbW0hn8+PvOJhmvu5VOVyWSlKUjGT8k0PfyI6nQ5qtRrK5bKqOuf1epU8AfoNFNlHbxzAwkmUm2SqpYElqzXq3jG+J2GapqqWKY1i/i3nFxVchppaKW3yt4zq0OcqQ3enp6dRrVZRLBaxvb09VlVNHfTD5XIhHo8jFovh3LlzmJ6exsTEBHw+n6pGKpunW+25OnRji5/To4b0fV731sn35JxlFA8A5WHzeDyYn5/H/Py8qmo6ihEyL4JBemEgEFA9R6ljdrtdhMNh1cdxcnISqVSqz2gm8c6WNcwp5HjX63U8ePBAeT1rtRra7Tbu3buHSqUyVmTuYaS/Pket7ov6bTgcVm163nnnHdVTeGpqSsnmSqUC0zTx6NEjfPjhh9jb28Pm5mafPqCTvqcBx2B7Bt3IcLlcaoGkUim89dZbmJ2dVe/zATHEgMx0s9nE5uamMuIOM1L0sD6efxAby2NeBMFgUMWOZzIZ1SSbYT3y++Q1Mo+K4xMKhZQiQcEsr1EqCnb3x++QJYX5OYbBMWFef18fO+aynT9/Hi6XC8vLy1hZWUE+n3+h8ToNUHDKilsM3avX6yiXy9jd3YXL5VI9rRiaQnanWCyqCk8kCmTRCKuiHONitBUKBdy5cweRSARf+MIXMD8/D6/Xi3q9ruYACygwTI8KtFWOJJVhHitJBgB9xXAOK3MsQ0cYcmoYB7lMu7u7WF5extramvJWjzI47/L5vDLY9JxAec9UvOT4MY+yWCyqEBz5TIADBU4v6z/qc5Ie/GAwqIrMsA8fQzqlZ03OPxn2bCVTdSJA97TRsKX3lmSN3ef4IytvynOxZUgkEsG5c+fg8XiwtraGfD7vGGxjDFZkPXfuHBYWFnD+/HnMz8+j3W6jWq0+1zdR33MHkau6J9xKb5HQ5748v76/U99g0bNYLIalpSU8ffoUxWIRrVbrlTLYDtPdgsEgLl26hImJCRQKBayvr6PVamF6ehqzs7OIRCJ4++238frrr6PX66ncdbYYCQaDioRhTvze3h6q1Spu376N9fV1VKtVbG9vK7KXPUfHCfq8k3KVRKxpmn2h3vIz3HuY5jQxMYFf/uVfxgcffNDXCL7b7aJQKKDVauHmzZv48z//cxQKBezu7vYRFo6H7YwgFVk56LJKXSwWQzKZ7OvLxF5DHo9HCTT2zvL7/X3hRIc9TKvJd5oTgEory3dLb5kMexqkSDGESnooJKTyIJW/QQYbv5/H0FiT4RQSVkYIK6jF43GEw+GRZvDlWOgx1bx/4KBaJsvT8/9SqYRyuazy31hMQHrWxhWSfWUlTOB51lYWWLCaY7q31srbaKew6GtQJx30kDbKBpa9HxdFg0Yq15tkwuUxVutXenClzLOTIePm6QX6wxEpK2WImDxG97TJdajPPZ2V1XOCrAw2mXMp56GVV00akPr9yH559No5GF8Yxn4Rj1QqhUQioQpQyPkwiLQ7TOewI5GtyBtdftgRzXK+GoahWmOw/QAARYaNi/fnKLCShV6vV4WFdjodpWMyxD4ajaqWDJS11FWlHkdwjBkBUSwWVRRIrVY7M2PjJHESsopjzyqayWRSpWAA6CMVarWaan/AYm5ybz+r8XMMtmeQygU3yVQqhfn5eWQyGSwuLmJhYaHPlczNl6XCDcNAq9XCzMwMKpUKyuWyqsAjk30Ju79l2KGV0nkSCIVCmJubUxNVVh/UPWX6bxqtZGXpQeBvFnRgwQuOkZ7ULgWFNNio4NDwCgQCKnyHrLZe5U9uCF6vF7Ozs4jFYmg2mypZetQgjQh5LyxAoBu1NNSazaYyZDim9ADQo0GDbRD7OepoNpsqpHVychLr6+uq8Ssr3XHOyTkjjVQaINJDJA1kaRxbGWf0VPR6PRXiJ8fVMAzlda7Varh//z52dnaQzWZRqVTObrBeEAz7yOVyqsCNvq5kdTmZtxeLxTA5OYlEItFXkp6eT6mQyefDuSzDtEYZVIpkrqzs1Sf3EGmYyZ/DvIlW8lHuB9Jbp5MS/M357Pf71Xxn0ScZDcFQ/0AggGq12tew28FoQlfEJXw+nyoglkgkkMlkVBEcelY5V2nM2xEIVt9nN2/tXrcyzuQ+JwkNYF9mMELk7bffxtTUFFZWVlAqlVCtVp+r+GxnBI4TgsEgQqFQn4yYnJzE/Pw8ZmdnkU6nkU6n0el0kEgkVEuGcrmMzz77DM1mE1tbW6rhONc+9yopj+hl5bOPRCLw+XzKIBmnwk9Wz1ySBHb7Oo9h9EcgEMDbb7+Nr3zlK0gkElhcXHyO2Mhms/jJT36CfD6PO3fuIJ/PKxvgrOEYbM8gE7uBfQ9UMplUVRMZZtBoNJSrmXkw7GlDTxvz2ra2ttQCaTabllW47IQNFW49xOWkhFMoFML09DTS6bSqrKgbbIO8P81mE48ePcKdO3fQarVQLpeVR6FUKqHVaqm4YAoLVpyUhrFVngYNPZakZSWpaDSqFHVZcc/KYJuengYA5HK5kTXYgOfDOylM9E1Qblb8DI0FmZMin5P+PeO2sXE+MdRnc3NTMYvJZFLF97OyoaykJ72+en6QzGWjAsNNjp4MafTRKKRRLNcHn4HX60W73cbDhw9x+/ZtReyMCxgSube3h1Ao9JzBBhx40CmLaLBFo1FkMhnE43Fbg003XICDOT0OBpu+ydMz5Xa7FWEnDSvuDcABU8u5oleP5Pnlb0IqtfoxVufQST0eU6/X1ZzmNciw+O3t7b7WAg5GE4MMNq/Xi6tXr+Kv//W/rnLsG41G39zU9xCuTZ3cs/KK2xHMg2AVqaDPWUmctVotuN1u3LhxA++++y5u3bqFDz/8EE+fPlW9MeXnAOt843EAvTvJZLKPbJycnMTs7Czm5+dVNV3TNBEIBBAKhdDtdrG7u4unT5+iUqlgZWUFe3t7KJfL2NzcRKPRQDqdxvT0NAKBACYnJzExMaEKlDHcPRKJKKJOFqIZB9g972HmAfd6eixv3LiBv/E3/gaCwWCfvsD5lcvl8NFHH2FzcxPLy8tK/38Zc86R0M8gw0qAA08Hk8vZ+LnX66nwEW7eeiEM9igJh8Oqf1S9Xofb7Vbslp4rw82c38tebzIPgYvqJJRvr9erKgqx2AVhxbJJAS6Lq+zt7fUx5TIPiGFshmH0VZu0aiTLPBfDOAirarfbKBaLaoFJYa1fm8620QBl4RhWQhv1OG2756rf5yA2X55nGIZ0lME5Uq/XUSwWAUAVVJFGlk4ocA7r80xPkpceTr2ohp4PJ3+Ag2dA4V2v11UO22E5cKMIPSRSh5UMcLlcSKfTmJ+fx9TUlKqgy/w0qxYS0rCQxvMog/KZ8kT3eOmeMNmUGOhPSpfVSnXCRkKOy6C1K0kK6V2XjLNOaPBHXz8ORhu6ngJAkQfpdBrRaLQvn5zeFDlf9c8P+h4JXT86yjXrf1tFM/B6OUfZy5EEiWmaqi3LOEI6A/hDXVEazKxiXKvV+ghcGQpN3atWq6FSqaBer6sffpZeM+qPwEGvNsofGsnjqBsQcuyk44U/MtqIP0xzikQiCIVCyuEii8BxDZB8lYbty9rbHYMNB/kbUiC5XPvl7umWloU5XC6XYoL4NxM7XS6XijPmpKBXbnd3F61WS+UdsZBGvV6H3+9HKpVSSe0TExPwer0oFArI5XLqOwqFglpkL6LoxONxXLlyBdPT05icnFSGEu9dGjcylIfKXLvdxtOnT/Hxxx/DMAyk02lVnWhiYkK5kp88eYJut4u5uTnMzs7CNE0Ui0UljGQFNS42Gn6NRgO5XA7tdhvnz5/HW2+9hYsXLyrlyDQPSt3z2bXbbaVYBYNB1Y+E4SHlcnmkFEQpRORmpm+MND75vvy8VU6Wfj4rxW1c0Ov1sLm5iVarhUQigQsXLiAej6PVaqFQKChvjxXrRc8214ws/qErLlIRkPNREg0MK5NFM/b29lAsFlEoFJDP5xVRMU4GG43iUqmESqXSx8QD/dUdDcNQXnW/34+vf/3rePfddxEMBlXvH7/fr5QHGTIuCQWu8XHo/0W5PjU1hUwmg0AgoJSpUCj0nKdCl2dUkKhw1ut1Vf2tXq8r5crOIyxJLrlH8Rri8bjKe5mdnVUKroxg4DVLAsvv9yuZOU4y4fMK3eBxuVx47bXX8P777yOdTmNpaUnJKMo+2VDZNE1VfETOJRkGZuX5Pelrt/oOhlnTyKCiPTU1hQsXLqgwc8oLnXwbZUhCPhaLqTLxwMH1cx/L5/O4e/cuNjc3MTk5iaWlJRX1wHSI+/fv4+bNm2rfo/zmeqfu5Pf7kUgkVEj/1NQUkslkH5m2vr6uiPdRgdVctALD1Ek+yYbu/EmlUioPkMeyeBNTbbhny8JtPFexWMTm5iY2NjZQKpVeqv7oGGzPoAsResjS6TSSySSi0WhfCIzX61VCo9FoKGHi9XqRSCRUhcmpqSkAwN7eHtbX19WxNN743Wy2y+Im8/Pz8Pl82NragsfjUew9S/O+aPPjUCiEmZkZzM3NqXL+VOytGDgqILKpazabxaNHj5SCFgwG4fP5EIvF4PF4kMvlkM1mVYWjZDKpFDWyOjInSIax0Wjb2tpCLpeDaZqqtCrQnw8j2UQyitykGJYZjUYVMzVKBhvwfJiLlRcHwFA5JoNYUF1pHiewr1wqlUKtVlPkQDAYRCAQUDmSumIrc9hkOwBCD63R+4XJecZ5r8+7SqWi8gioUIwbuC5rtZrqUccxlMYax0Tm9b355puqdyM3PtlEWp5DbsRUNsbBG2kYRl8LFObmkkixukfp5QWgFK5Wq4VKpYK9vT1F4JVKJUUIcGyloUcDWnokSVYFAgFMTU0hFothYmJClQSXpJsMOafBRqV4UL6rg9GDbuzMzs7i/fffRyaTwczMjNo/+SOrsspUAv1cdt9zEvNC/x4rg00SDDTeSJxPTk6q+TrMtY8apDc+HA6rdgtsy8P7ZUXP9fV1ZLNZuFwuXLlyRVV+ZEXHjY0N3Lt3D4ZhqKJBsjAXCTVW2GQ0SiKRUJEjlC+VSmVkC7Md5g2mPGO4OlutUC8IBAKYm5tDMplUferosaSemEgk1Fhw32J0hNvtRq1WQ6FQwN7enqpQ/bLgGGwaGPrIjueRSOS5fjuSYafHjTk1TOamgkc2iMJHNt8m8xyJRBAIBBQTQNaz1+spj1Wj0UC1WlXst5XyeRTIfkI0BHQhqoff8W/p1QL2Q0Dn5+dx8eJF1QiT4Zz0kF24cAEXL15Er9dToaZer7dP+aHiXC6XUS6XFfNMg01nl3k9EjxG5nwFg0E13qMU+iPHUhoFcqzlsfpnB53X7u9x9LBJdDodbG5u4rPPPoNhGKhWq5YGr1UYI+/fjuGVc5DnGgRuJrVaDfl8fmyNNYJrhkaUnZdQjjGNCQDPFSXRjyPho5f5HgeQ7MnlcjAMAw8ePFDEnWRtKUsrlQqq1aoKbyKhUK1W0W63lTeT4Tb0sFFxkHPYzsNGucwwnXw+rwy/SCSimHySYnpPRio7bP8xrjLh8w7uo9xLiUEEnd26lhjWy8FjrPbiYb5Xvq6TQx6PR0Ud0UM8jpAGmxxXko4AVLNwFgNigS3qfMViEfl8HuVyWRUZsSO+aIzQwGUEGb/f5/MhGo3C7XYjl8shHA4rA38U9jCrucd8fZ/Ph2QyqVKV6D2THjYZ6sjoL7/fj1gsBp/Pp/Y6ykLqhlb5vydJXLwoHINNwDAMJJNJ5Q1iHxNuyMzTkhtfIBBAr9dDPB7HxYsXVd4VPWLclJvNpnKJp9NpxGIxAAe5VlxgLpdLbebdbhcTExNYWlpSygEAtekft2Q4Y3gnJyeVO1g2t5WGAw1UQubZkZWJx+P42te+hq9+9at9IRb379/H9PQ0Wq0W3nvvPbzzzjvodDp48OABNjY2EI1Gcf78ecTj8T5lem9vD7u7u9jb20Oj0cDKygqAAwaO1yEhwy2YX0ejNpPJ4Ny5czAMA9vb28cas9OAdM3TpS/ZRaA/PttKcNgJESsjZBxzVvSwxHq9ju9973t4+vQp4vE4bty4gZmZmb7P0HCg4S6JFs5pWbpe/khvm12xDOBAsej1etjZ2cHdu3eVB3wcQXaRjdeZh2e1gQHo87Rb5UBIg4PGivTy2LXpGFW0222sra0hm80iFAphdXVVVW1bWlpSCjNbv3z22We4e/euavxLz77MX7PKFbTy7Oo5SFahlvV6Xe0RoVAIPp8PX/rSl/Crv/qrSKVSin0GDua21+tFLBZDIBBQxaEcjBcMY7/oz9zcHCYmJhAMBvvC3yW5ytet8uClwWVnVFmtcSty0Q5yj5aeE8oYSWByn6KewqJlo1zJVN+rJGQoMnt7ud1u5bX3+XyYmppSMuX69euYnJzEgwcP8Fd/9VfI5XLY3NzE6uoqms0m9vb21LhR15TedxLyrLdQr9eVAefxeBCNRnHp0iXEYjG4XC6srKzA7/ersvUvk0yT+63cu1mZN5PJ4IMPPsDi4qKSdxxTRlglk0kkEgkAB6GmoVBIhbPn83lsbW0pfb5cLiv9U0Z6cR87LtE9aE4cB4cabIZh/HMAvwpgxzTNa89eSwH4QwDnAawA+HXTNPMvfDUvGYaxXwWOpe6ZlEj2URYIka5YAAiHwwCg2FQaU+12WzGghmH0CSIyAQxfkQUOGErIrvbdbhdra2uIRCLKVfsi4DWEw2GYptlnsFkpplIwy2p6wL5X8ty5c7h27Vpfn7put6vCfi5duqQMT8YIJxIJvP7660in030G2+7uLpLJJHZ3d5UxB/T3vrJaCHIDkEx/KBRCLBZTYz4qkBsUFVlpRPAYu7CAQYab3XnGybumG0k0ZtfX15HP5zE5OYnFxcXnDDa5lqRyIjcBOaZWnuRh8yPoYcvlcmObDA8cVH6kh01X6nRlTq41afxKBlmOJUNJ5TMZJ5jmfkg2w96r1SoCgQBmZ2cRCARUhIXL5UKn08HTp09x+/Zt1Go17O7uolwu255b9k2UhIpstULmWPZ/oyHcarWQy+VUqw9gX1GYnZ1VbDkJQX6Oa4DRDfztYPxAbwk9NISU91brV1+DugEnz6O/ZnXcIOhyw+p9/Xq5BpibKasgjxqsImD0MZTEC8ef98eKrVNTU5icnMQXvvAFzM7OKm/a5uYmnj59iuXl5ec8YHpTcwDPefxZSIP7ms/nQyqVUm0DotGoKlpip2+cJazGk9EM8XgcFy5cwBtvvNEX4SB1y0wmg3Q6jW63q+pEhMNhTE1NqeMZMkoPG/cvqVta5XEf9x5OAsN42P4FgP8XgH8pXvtdAN82TfMfGYbxu8/+/69O/OrOGIZhIJPJ4I033kAymcTU1JRK1pVeNRlWpQtAspYu134xEhneyB+WVwWgjpOTw+/3Y2FhAQBUHzgagtlsVsUyHxXMd2BRDisFTY4FIdkFKl/S/V6tVvHJJ5+o5HuGIm5vb+PJkydKWGxubqLT6WBtbQ25XA6hUAhPnjxBJBLpM8IYd824YY4NixRQGB1mtHEBxuNxNBoN7OzsjJzAHxS2oh9n9f+gz+mCxs5bNKqweq702DQaDVUNi+0iXC6XKswivRf63JaeDv7Pua2zvVaeNZ6fG9wohJCcBGQPRYYty4qXXIfA82w5YB+eLOehfG2cjDePx4PJyUmkUimlZDH8Wxa/4fgxLyQWi2F+fl55twgZLSChe8al4ioJHp6D48iQKOYRmqaJRCKB27dvY2VlBT6fT1VWZaQIwzW73S5u3bqFer1++gM5AjjMk3SS33Ha38Pzy76I+jqz29eBA9mnG3WDrtnKe3TYsYMMNV0p5nXRCzUxMQEA2N7eHlkPm5SD/F9/n2Hhco9hPpvH40G1WsXq6ipyuRyKxSIikQju3r2LlZUVVSNh2Px7OhToVKD+Sm+cYRiqR3A2m+3L/ff5fH0FYF4GdFKVcoth6Xfu3EGlUnkur5q6Jp0tLMZCXZzezEgkogxVViIFoHKI2+22ijTZ3t5WlThp2B3FCy1/vygONdhM0/yuYRjntZd/DcAvPvv79wH8JV4Bg83tdmNxcRG/+Iu/iEQioRK5pTEhIYWNLHgxOTmJTCbTl4Qvj8nlciq3q1QqqfwFHseG3aFQSHVeZ2f1x48fK1b1qGDFnFAopBQNGn9WQpP3zIVsGIba7GWp2Hw+j3/37/4dfvSjHymFmgo0lYsf/vCH6j6pBNJbpxsSrOrYarWwurqqxq5Wq6FUKsHv96sQHn1j4j1QYWdPtmAwiN3d3ZHqNcR5pbOgdn/Lz1n9b7fhyrGVIVXjAH3DoOButVoqtGFvbw9+vx/RaFQ9X5Ig9BzJMdFzhTg2DKE1jH1Pu2xgrhseJDpqtdqxQ5NHCSSSGErM/kCsmiWNNgldVgD9pAn/1+em7gEddfh8Ply9ehVvvvlmnyFfLpexs7OjKgSz71ypVFLy9utf/zquXbvWt5YpwykPuU/IRuJ2yjbPwxArn8+HRCKBUCikQjCbzSYePnyIb3/72yqXToZP8rspxylbxx3DGB7SqDlMIT2O4SWN7WE99S8CyjN6sQmpn1itW74vr1d+hq9bkWY69H1qkCdP6lM0ZKgv8BzMy/J6vbh48SJmZ2eRzWafa0E0Shg0PpKg4ZxwuVxIpVK4cOECOp0OHj58qPqo7e7uKkKwVCopnWvYKA5GULElAlN3WKG7WCyi0+kgHA5ja2sL3W5XyRJWpGw0GsfSM18UVnOGMqrRaKj8YObd6e2udE+Z1M1JeH3961/Hb/3Wb2FycrKvn3Iul1P9XR8+fIh8Po+HDx+qPneEXBeAdbTTMPLlqDiu9jplmubms7+3AEzZHWgYxm8D+O1jfs+Zg4oKN0ArBd8uvEBnQJknoLPK9AxIg0aem2WaI5GISrZlxbV6vX7sYiP0sDHHgULESqjryoHdRkhmY3d3V4VVUuFgsQ8AyGazqngIIYW1VPzI9Moy6tz8JOtjxWLp/zPPsN1uq7CiUcMw3jWdwePrw3jaXjXw+XMTk2XJ9Z4rQL+nR65DXSnmfNTnvvS8yWvgd78sFvKkwfVFZpsheXZKK+elVZjtMMy6zq6PMsiGp1KpPhnC65cymffFAlOZTAbz8/PqPMBBgRd6uejpopJ0mLJtGIbqCef3+zExMYFoNIparYZgMIh6vY6nT5+iUCigUCj0VVElyaATjuMc0msHKzZ8GHnLOc35b5dzeJzvPykcxSumf47Q92P9XIPeP2wc7a5JP6euT0gvG4twsDjHKO7fxGHjr0dycAypIzabTRSLRZTLZTx58kT1HT0OpHEi5TOjU1wuV1/RI11/Heb5njWk148GFK//KLLLMAy8/fbbSoZKDySfDyNopGdNerAHnfs08cLuBtM0TcMwbGeqaZq/B+D3AGDQcaMCesXYw4QTQc9f0x+MnOjcABnKoiuK8XhcVVFMJBJ9fVEAIJFIYHp6Gn6/H61WSxXgkDkKx0E4HMZrr72GqakpXLx4URmk+mTndUgmmZNVKq/sF2cYhjJw5fiwzwUAJXh1xVkv/sCE0VAoBNM0MTMzg2AwiPn5eSQSCfj9fng8HqVgUEkH0HedFFSsvsmeeKMm8OWGK5l13Ug+riCQRsg4KMZHhTT0yZTJkDHOBX18JUMm1zjDVnTlRFc+ZEjkcRS5UYNpmspQoPEgoXvSdEbRbn7qhI+uPFDOjvr49Xr7VRhXV1dVFeFgMIhgMIiFhQW0221EIhEkEgmYpomtrS3s7OzA7/djZWUF1Wr1udBGhndJBc6uzYGumEvlyjD2w81dLldfoat6vY7XX39dGWv0KtdqNRUilEqlEAgE8OTJE3z66acol8vKOBlXHGbE6OFWEozIYMXmxcVFxONxLC8v48MPP0ShUDj0+60IopOGz+dDOBxWOT2hUEhVUdRzSinPZAEHSSrLtWlFYnGsdCNOGlmHkac6rJ6B3kia12xHXIwTZFQHPTU+nw/tdhs7OzvodDrY2NjA3t5eX3E7K7JwGEgPG9NuSBJRzm9sbMDj8ShvO1NlWFH7RQrbnSZklJaUlUchMGq1Gra2tuByuVQeH3VK3jOLNWWz2ed65hFW/+s6CdfkScjU4xps24ZhzJimuWkYxgyAnRe+khEB8xCoSFD4cbFZVezSXbB8MLqw48OlBw1An8eID5qeKZfLhfX1dWxvb6uk8hcRWrFYDNeuXcOlS5cwPz+vyqHKuFyd7dKFvbzeaDSKmZkZmKaJcDis4qWZJyc3BrLTUghLISYryNGNDwCBQAAzMzMqf4SbEj/PsCAJ3WDLZDLKOB6lfiNSkZXCRzdgeSxxlM1LGmyviidIQo6TrLgJ9BtsMl8N6K/eRONE9jYcxAID6CvH/irksJHhbTabqgEr8LzBZbU5Wp1Lvi9zf2VI7jg1zu52u8hms3j8+DGi0aiSc5RrHo8HMzMzOH/+PAzDwO3bt3Hv3j3UajU8evQIP/7xj5VC5Ha7EYlEkEwmVX60bK2iE4Ocw0B/r0COWavVUrnNMkRodnYW169fh9/vV/JFevGSySRef/11JJNJfPe731VV0xqNxlgbbIdhkPz0er1YXFzE5cuXMT09jZ//+Z/HuXPn8O1vfxsPHjwYymADng/lPmn4fD5VrjyZTCrjTaZLyIgDKeeovzCPUV/fdsaZ/podrN638sDrugY90nJ/H7fQaTuQjAGgUmEMw8Dq6qpa29x/uPYlmX6U+USvHeeEYRhqTTOdoF6vI5fLodfrwe/3K/Ke3kymy4wiuFfppKEkAw+bL+x11+12VessOlHoQGHhkvX19UP7/+n6AgC1R8jxf1Ec12D7twB+E8A/evb7T174SkYIVgJd95wB1vk10rixO5f08uhVnYCDqmFSiJ6UwJIsrxVDwd9cAFR29bwK41mIUCaTgWmaqmk2F73sLQagz/iVAlgabPxhmA/HuNvtqmRRO0VR90jJ7zlq/PfLgr6BneR5eU59nMYdenjJYfcmGTBpgNgJYTuWnArQy0zMPmkMq5ydxPzRiZtxUMbYJJvEFH/o9WejVsMwVO8oht2QSKAHmJ+lsab3wtQNNv6t98fjb5/PpyqecUwZ/i4NNpm7ye+VpNc4PIcXgTRi5P7E8N9QKIRUKqXSIuLxuPqJxWKIRqN9XorDII3xk+xxJSuGSgJZv1fdEOPr8vdJQcpWO9jJXKu9ie/T4HyV9izCSomX695qrxoGugce6N/PqBcxd42f4ZrQQ+FHES+iK7EnJguLyPNImcrfh0VmSccNSTldj2UY+otgmLL+/yP2C4xkDMNYA/APsW+o/ZFhGL8F4AmAX3+hqxgh6KFVrOojFw8XmTR4OPnp1dFz1yTkYtSrTgL9LCtL9jabzb6mmMdBq9XCzs6OSkCNRqOq2hmNIekZ4xg0m00VKsPN3ufz4Wd/9mdx9epVAOjbBKW3kaCQ0KG/xknPUFK6kumV1I07vWgHFRYyGpVKBevr6yiXy3j69OlLSaIdBCvlV/em6a8PI6B0DxJZNz77USq+Mix0gcoCCuVyWYWPWHmEOG/1ZHqdkSPTyTlk5V3nZ1lFSq/YaqUgjRs4ZtLjqL8PHF4Ri7859rrywZCbcQgp9fl8eP311/HBBx+osCGuJV4/25i4XPs9g5aWlmCaJt58880+Aoxr0UoRkLL/MEh5x5AnMsTci7i3sNcmy1zn83lVgCAUCuH27dsoFAqW4bDjBLnPWM2pcDiM+fl51Vg3Foup1jlTU1Pw+/3IZDIqzNDv96NWqyGdTuOXfumXcOPGDdy9exc//elPB4aLcexnZ2dx8eJFeDwePH78GCsrKydG8Eg5JmUZ5w+NObkX6OSdlHnyvPIY/XWr69D/PozokaSD/jrntGmaKpXC5XIhFAqNXErDcWG1xw8z7sPKSVaypYziHqh/B+eAbLLNPW/UDTYdMszWigyQ+ng+n8ft27exs7ODZDKJN954o29MPB6PaunFSpJer9eSoGVfO7/fj+npady4cQOJRELtoc1mE5988glu3rz5wrJ1mCqRv2Hz1tde6JtHFPRAySREXRhSAZE91pg47vF4kEgkVH8zO4WbP2TfKKT4Hfyha5uND18ErVZLVdTzer3IZDKIRCKIx+N95f5ZMl+Gl7GkrGRnr1+/jkwm8xz7Y+WZ0I1U3fiQG61cYKzGx2aRtVrtOWPXSklneNfe3h6Wl5dVo8RRDF8bZLRJ5VcX4nbCWxfwFGRkf9rt9kiFhh4Xprmfb1WtVlV+pJ3BphMGkl2XYSgSFM40XKTXu9Pp9IVEWikfo26E2OFFvF866WT1w/eZUzEO4Xderxfnz5/He++9p6IOKGeY/E5jyO12IxgMqhDu6elp1cSV4LwE+ok/fbwlIw48r2hxHpNcY25lp9NBNpvFxsaGIhQ43iyN7XK5lIG5vLyMSqUykjkrR8Wg+RoIBFSrnGQyqSoIX758Ga+99ppqwCvDThuNBhKJBH7mZ35GVdy8ffu27VhJ43xiYgLXr1+Hz+dDrVbD06dPT8Rgk/uo/iPfl/1SrchRPeTebl/RjUGr/cjqWDtY6Qbydf5IAtzv978SBpt+v7ouNIyRfBioyzFMFoBl0QzOARps3O+s6jSMOuxIUqs9uVQqYXl5GYVCAdeuXXtOd/B4PCqklIYbc+H1dUSCLhqN4sKFC/jKV76Cubk51Go1lMtlVCoV7O7u4rPPPjt9g+3zAilkZWNouYnqxpfcTGXBAp5LKotWxprOBkjByvAWMh00oILB4LGr03U6HZRKJRUCEw6HEQqFVMgHwxnZvyMWi6nCJxSe8prlxmY1RsMYbPpYyoVDFl4m4tOwpRJSLBZVBU0yzDKxPp/PY3t7W1VfGoXwNZ0IGIZNsxKehx2rjyu/W5+j42xYSKXfilWTYb1yPUtyhMfJuaErDnaepnEdu2Fwmvcmn8M4jCONs2q12ldVzOVyqZBIyivKIObo+f3+vhxgOafk/iG/S/9uOf+sZCm9KbKVTLlcVrnYjFDw+XxIJpOKXAyHw/B6vSgWiwiFQqqFwzgY0VaQER4MSSIx6vF4kMlksLCwoMjKZDLZ14cJ6M8TpCxlf1VWKxykzHJcGR1Dhe8km5Nz3nG/tlKwrYhAfb3ZXY/V68dZo/reZmUQ6roRv4tRDfJv5refZHjpWUM3dnUjWCe2jgsa7Jx3bB9CWSb1Kp2gG5cwdeB4Y9Rut1Eul+F2uxXBJcMa5XOglzccDqscUZfLpXq9sXhTOBzG7OwsEokEIpEIqtUq8vm8at11EvjcGWxWhhIZBiZfUsiSsZQGmc5KydwA2S09EAioBG+9qIZk92Xpem66rVZLGRecMOxMPzc3h3q9jmw2e+RJUC6XcevWLZVrwb5V7Mvm8XhUO4F4PI433ngDmUxGxfX7fD41Hr3efq8hLngqzVZMn/yt/83/rZRtKuSyiIvX60Wj0VB96W7duoUPP/xQFWThj1SayIBTgXmZ4EYrY511A17iqBu8nNP8Xz4XvZkm5/a4CGcJNijO5/MIhUJ965NGGpUZrk+OBTdH5phwQ5PMOue5nqsmiZVx2tiOAt3QHwY6izno8zRuxsXD1ul0sLm5idu3byMcDmN6elrJz8nJSbhcLuRyOWxvb6PVaqlwWbfbrSroMm+C+wQhZaQMYbMiZORvq/AcacRJAvLChQtYXFxEq9VCIpHA9vY2wuEwZmZmEA6H8f3vfx8bGxvY2NhAqVRCoVAYCXLrqOC+5vP5MD8/j9nZ2b62B6lUCm+88QaSyaTq9WWa+yH+lUoFAPqq9NGgjUajSCaT6HQ6+NGPfjQwQiEajWJpaQmxWAxLS0tYXFyEYRiqp+tJ3efExATS6TQSiURfRJBukEkFXN6zVRN2HdKIkK/J96z+tiMQpd4j35OeYrnv8zp7vf0WSVNTU6hWqygUCqpoxjjCqv4Bf9t5OY8C5lAFg0HMzMxgenpa9fHd2dlBoVDAw4cPVcl62RNSkk6vAqyM32KxiEePHiESiWBzc1MRcYZhKL2M4eU+nw8LCwvwer0qqiYQCODnf/7n8dZbb/XlAScSCSwuLiIQCOD+/fv4y7/8S+RyOaysrJxIqPnnymDTDQmp3FJ4Mc+HyYJ2oSo6E0SjjYJTJnIS0liTpcb5HZww3W5XVfySle+CwaASzsfp0dFsNrG1tfXc6zTgPB4P0uk0YrEYJiYmVCI7NwWdeaAxypBQabDqnsRhoRt4em4gKydVq1VUq1U8ePAA3/nOd16oZ8lZgkaCTBYfZoyGGUO7DVfGrnOeS8VwXHNWuMHIvlK698Gqj5j05EoiRRpzugfEipU+jlEzTjjs3nSmeBCsnoHMrRj1MSRBtbW1hVgshlgshmAwCL/frzwo5XIZjUYD9Xodu7u7yGazcLlcquFvs9lEoVBQ0RNynvJHLyJhN27Smyc9d5IIIvEWCoUQDAYxNzen5CnbnFy8eBHxeBw7OztIp9OqUfq4ggQkFdWlpSWEQiGcO3cO6XQayWQSV65cQTweR6VSwd7enmqBwPw9eh+o8HLvicfjME1TVXC2Aw3EdDqNqakpJJNJAFAFaU4CNCI5D63yxvW5I70ozNnV9wC7PcTqb/4/zD1ZRZLo8lTP56fBwGujVyORSKjomVfFqJDQ5e5xwkCph3m9XqTTaSwuLqo8VoJRAdKjLMnJUZfJR4F+L0xhqtVqKJVKfTKZejjTnki6sSYCc9auXbuGr371q33kjfTANxoNPHjwADs7Oyql6EXxuTLYCJ3B8Pv9qkRuIpFAIBCAz+d7jlXXQ9i4KMjQSUZfKoXSe0Jvm7wWyYR6vV7lNWg0GsoVW6vVFBN9VCPoMEjWjaVc3W43NjY2lHBfWFiwdOPzXuyKgPB6jwIpuHV2jmE/5XIZpVJJhUOOE/RQMDvheBxDd9B5JHMpC3GMM4bJtbLyWupzlM+BYTb63LZSoK1ee5VwmJyxWt86Qzzo8wzXkXk2owp6p8PhsFLiGYbNkHFu/vV6HcB+n0qPx4NYLIZQKIRWq6VC2vX8IxmOYxUqTUiSQIaky5Bxud/Qo1cqlVThqGq1inq9DsMwsLu7i1qthmw2i3K5/FLaVFh5EocBDdtQKIR0Oq0KhsjenbOzsyq8n6FhzIOmd4FGr3ymNFopJ6vVKsrlMlqtVp9nh89LFh5YWFjA66+/rnqUsthVpVJRkSz04p3E2A0zV4YZVz2CwOqc+j6jHzvIO8R5Lvc9nZywmgt8nx5hyunNzc1D7+lVgJVRfNia4RixhH+lUlEOhVgsprzJJClJhpN8H9eom+OA651jyv2ItRwymQyuXr2KUqmkjLxgMIjZ2VnloAEOoh7K5TI6nY6K6JKROy+Kz5XBRqEshYZpmkgkEqofDRtlBoNB1Ot19SDJRujnYjIimU2ZS8Bj6G2TFZAkq8TJwXj0ra0tPH78GMViEclkEplMRrEjp6Egyv4fjUYDe3t72NvbU+X033zzTbzxxhtIJBLPKWOy6pmufAxa8HYbDNDfhJzsjzRqW60WNjc3kcvl1HWOC7ioAfQZTyclIK2YTOmtbDQaSojIVg3jCBqgvBc7w00nT6RXjfMMgCoiBBzMbQCq2I80LOQ5Rt3YOA6GJYV0A4GyVW5i/C1ZfuYFMAdg1IsJuFwuRKNRTE5OKg8WDS+GPmazWaytraHZbCKVSmFmZgY+n0+F43G+6oYVMBw5M+gYzntZdGRzcxPLy8swTVOFRfV6PWSzWeRyORSLRRQKBbjdbty9exdbW1vIZrNoNBqnJhOsiCW5XwzL7DNNwO12Y35+Hh988AEymQzOnTuH1157TSmgMreQ+0g2m8Xe3t5zxgyPbbVaKJVKCAaDqjLx7u4uHjx4gFKphPv37ytG3u/3Ky/r0tISJicnsbS0hG984xuYmZnB9773PfzJn/wJ8vk8qtUqpqam0Gq1sLu7i1KpdCJjqc8lfd887BxyXeoklNzrrZ6dlZyw2/sH7esy6kNGNkjP88TEBL74xS9icXERpmniwYMHI1f1+TSgR4RYva+DRbHcbjdyuZySWUx/oV7bbDbhcrkQj8fh9/vR6XRQKBTGPiRykH6pv9ZoNFAqlZTTgmNFGfL666/jypUrfSQZjVxZtp+1E3Z2dlCv17GxsYFCoXCitRM+VwabHcjAsbojWV+5kUilhA9eepP4Pw0YCh2dVdIVZP2zNPaq1SpKpRK8Xq9iZ6WH7SShGxF8LZfLodVqoVAoqEkqIRVgvZgF3z+q0cbXpYdN35h6vR7q9boK3xk3g0NXXk9qMQ8aT36vDEMbZ4FMyHE8LJwHsE5wl+ch4yjXr5zn+rleVYONOIrRBgznIZHEDAmuUQdJKUZfSEWXyg2LkrRaLVUhMhgMqtA14jDlSycGdQKGv/Vxp7HBnF0qDt1uF7VaTUUjsHG2bLJMzyDboZy2TNX3hmHXkNwvGZESjUYxMzOD2dlZXLhwAVeuXFHKJw01hqvyOXFcZVg61zKfqQwNY954Pp9HpVJRn6WXmFVBJycnMT09jbm5OWWw7+zsqHY6DF88jTkv58JR1iOhP49h9u5hPGvyegYZg/oc0Im2QCCAdDoNl8ulehw6sAbnMElI5mjFYjFlaEjdlrntNJjHOSRyWGONYK0JSRrIiDe2cZFOGxpnJLdIUlLWymiFk8zR/twYbLrSKjdGMmput1uFHtJjpDPF/Ft62GjwSIZKen10o4MTgYwolWhulh6PB3Nzc4jFYmg2m9je3katVsPW1hZ2d3fVhnuSY8ProxKvM+Nc/NI7KN8/zcWtC3Tp7te9a8fZqF4W7Daqk/4OqyIGrwLkWmbxHq4NWZJf/s/PUYiS8eXmRUWK3kjpRWeeqiQ4XkUMMy+PMm91LxxlJz1sgUBg5D1sVBgjkYhSbhhVEQqF1EbPSAjZF01WhtT3CcA6Kd7qf14Hf0sPsSSBqHgx35dzlZEewWBQMesM+4lEIggGgwgGg6dmtNkZ9lZ7sgRLZjOpnz3wWPhlYmICV65cUaGnlUpF3YOUAzSypLEkv4/PKRQKqX2axprX68Xc3BxSqZQKt6SeQKONaRWtVgvf+ta34HK58JOf/AR7e3vKcJZG5IuA98R7tBpjScrpv09j77Ey2uTctiIZgH7PG3UMrhEa0Jwfr3pkwyActh4ZpcU5WSqVFIm0uroKv9+PqakpxONxZLNZRKNRzM7OIpVKKTlAz9y4FIMaBoPGrdPp4N69e/jTP/1TJBIJXL16FefOnYNhHHjPi8Ui7t27p2QmZWsqlVKFhKTeQA9+sVg8cT3hc2Gwyc3RyrPQbDaRy+VUrD/LIAOwbCwpBQ2Pk5uDLMAhvU4y/NHr9aprkUKc7y8tLaHVauHOnTu4d+8eisUiVlZWsLq6qj5zkmNDjxbHSN4nlWEqvlYG26Dzv8jGb7WxsNgEx9kqbENe/6jBykt70t4uuUHKPIFxhp0HjQZ8vV5XBUi4HjlPZGK19IrIhtv8n4oDS5+HQiEAB32sZMjz5xG6x8dO8ZOsOn8AqFAnsuThcHjk56Zh7OdKJZNJZcDTiIvFYoqR5RxkfpRstsr5qPdD4hzm9+heM6trAaAUM+lldrlcStGgwca9iJ5B9ghl2I/b7UYymUQsFkOtVlMRDGeFQbLPMAzE43EsLCyoZuTnzp1DPB7HtWvXMD093WewttttFAqFvjHhnkpFXxYVoGEqqxHH43FVRbLZbKJSqcDn82FpaQkulwvvvvtuX4EwjnGhUEC9Xsf9+/fxx3/8xyo8liy8z+dT+UEvSrjyXjmfrLyVJAh0Yksf30Fjz/McFZKckc9XygEeR4NNej/lZ7h2OGdl3tDnDYOehc/nQyaTUWuaYb/FYlGF+C4tLanQ6GQyqUIh/X6/mlPM1RzntgnDotPp4KOPPsLt27cxNTWFv//3/z7m5+fhcu03ag+FQlheXsaf//mfY2dnRxUBDIfD+Nmf/VncuHGjzzPXarWwtramnCuOwXYKIJtGQWDlNbJy10sjbJjwDqncWBkXFFKcLGRKWXmMzO1pKYpW9yjD9uTGcNrMnP6+1VjZlbUedUV60NgdNo/0exvmXHr4yquKw0I5DvM2kjmTc55zz6qQzqDiEK8CBhlhL3IukgmMNNAreY4i+LwZwimJOG7YUoHXvWZSedVDvSSJKL1udp43Ow+KVYiZfF0WNuF98DeNPz0V4Cyhk0scZ5mukEqlVNXiyclJTE5Oot1uo1ar9VW4k4aKXPNyPUvPOtAfqkpvJMeW4bAkcCKRCAConOBGo6FyVZrNJvL5PHZ3d5WCR0ORRrTMhz2uQcT1YxUFxP/1H/n5o3jIj4vDInD0uaqvD3keqaeNQxj1WYBrxuU66FEXDAaVN7fb7SoSiWF8LLrD9c81wHUjowJeddBIrdfrygvO8WDhoVKphN3dXWxvb6voimg0inq9/pzMJdlVqVRUP8yTxFgYbLpw0QWI7hGywqBj5ufn8c1vfhNTU1NYWlqCYRjPeb2sYq9l7zG5IVOw2EE3hPRrkowHq4qxT8ZJQ2ezpFeNlYUY/6972CSDpysm+vMaJLCtrkkKDD1XkB42nV2UyvQoCxs7r8Rxrlkfa/179GN1xW6Ux+kwkDnnemF7Cgrhbrer2mHINcdwR75Pz4P0NHPTomdCtv0wDAORSATRaLTv84cpJ+MIu7k6yLM26BwcH1Z9Y47uuDDmUgZRTtEooAeWpBqVS7nOrAg7ee+H5f9anUd+TnowGGavV0Kk8UHDhAU8WJDgtLxr8n6s7o3l92OxGJLJJObn5xEMBpFOpzE5Oany1cLhsGqTkM1mlceKz4SFAOReNKj6ptzPTNNU3gWOF72VfCaVSkVVgPv0009VERLmVDebTczPz2N6ehqTk5OqhxO9eOVyGZ988gmWl5dVCNpRI2aSySRu3LiB+fl5nD9/XnmoJNFEyEgg3hfvTR+Dk4DVvqPLRkl0cM5KsoM6FF83TROBQADnzp1DvV7HxMTEQB3rVcIgwz4YDOL8+fMqVDeZTMLn82F3dxfr6+vodruqciorRNIYYS/bQCCAcDgMYN84eRUMtmF1K5/Ph/fffx8ffPABkskk3nnnHUSjUeRyOXz729/G5uYmNjY2cOvWLVQqFTVn4/E4rl+/3tfjFtjvdbyysoLl5WVsb29//jxsOiuuewv4ICikBm1ydpifn8c3vvENnD9/XgkVbnIy9t2u8pnVtclqSLoSpxs6+jnYOd0w9kNuZB7OaUAabdJgq1arijkkk6grX7rBdhLQjVjdYOPzsYqzHnWmHrAuVqHPA75mdz+60XXYfetKif7aOEC/Xm7qPp9PGW1+vx/dblcxz0zyl5/rdDqq3Lk0uPx+P3w+H4CDct76d9Ogi0QiiMViStklxmUsD8Og+XQYgWYFqRxSWQyFQooVHgeDTd435Z705DA0jqFEhxlsVp4RfZysPA1W18P9Ru5T9PbI8CYSEPxbhumHw2GEw2GUSqVTk6OD1kc4HMbly5cxNzeH8+fP491331VNx+nRqlQqqngKS5bLfZbKpwzxH/TdVjJYGuEka6S8ZkGwYrGIv/iLv8Bf/MVf9IXmLyws4Mtf/jKmpqbw2muv4Ytf/CKCwSDK5TIqlYoqBsMQyuPkCyUSCVy/fh2XL19GMpm0NNikccT7IBnNeXVaz9lqXPV5Lo01SZRxHlNu854CgYAKVZ2YmPjceNns1j+wb7BdvnwZFy9eVKQL1//a2hp6vZ4y2ACoHoOsYlgqlVQ7K5fLpQy203AOnCXk3B50L16vF++//z5+53d+B+FwWM21x48f41/9q3+FH/zgB30pFDxnOp3G7u4uOp1On35aqVTw5MkT3L9/X1XrPUmMvMFmh2GUVEIyOTKWnQwO2Qm/368MAd0boXuRAPQZY4clwdoJsEGeJ26k9BycpYCS3kLdEzhIgAz7TKwUPjsG1spbN8wYjiJ0wuGw44a5t+NsulKgjdP46ZDKMgkWWSBnEFkiiQfOc9nXSs8rksocFbpxCOcbFjKhWvfgHKbg6cSBvn51OSpJsHFTvOzYW85Fq3w0edwgL88wsNv79NdkYYpBocLAwbM/qfkslXGSJ3zm+rXyvYmJCaRSKVVYhHuyDPvTc6DsDFj+r+/ZVp+zO97uh0QRjdxEIqHkhmEYyGQySCaTSCQSqm0FSaVOp6N6q0ajURiGgWKxeKzxJdlB75kVKcB7soowsprHpy3LpBGpE7GDrm+Y5/eqQ64BzqlUKqW8ziQvST6SCKPOa/d8deLp8wAWvGIFX3prC4UCKpUKtre3USgUVF9iHXpRJo6fjLI4jcJNI2WwWS1QfRLZTTi7gXG5XEgkEqrMsqz0lMlkEAwGcfHiRYTDYbTb7b4kYfmdVkU5qCTKY/id/FvfALk49AXCzYxCViZAX7lyBblcDpubm3j8+PGJTwI75Z0bvewVphcP0M8xDGsnjRZ5vGREeG658evjeJgSMqrQ4/B1g2KYjfQww8/qHHKdUDmzMmpGFfo1ssAPGW+GR/F/5oqwIqtkn2mUyCp6jPOn4OV8lxW0ZFNRn8+nCpWMO1gEJBgMIpFIKIJIEiRH9brxM/r87vV6yrNJ9n8cjF4rRZjgnGk0GqrIjZ2HbRDkmMk9UMpnKRt1Q1rKSYbbcU9jtIge7sRzMD/jRRuZezweJJNJFWI5NTWlcmsYRiwNRLZKCIVCmJ2dVQooADWe0qjRDSfpKbTKyZHPjrAiIq08nnyf4wZAlfnOZDL4tV/7Nbz77rvqvplLFI/H1T2xxDdlTyAQwMWLF9Hr9bC9vY1yuWyrGNqBJdoTiYSSQ4RempzpDXZG21nJfplbyLHiPmj1fPQ9ju0Z6Fn9vBgXnDc0woLBIF577TWcP38eoVAIMzMziMVifZ6gVCqFK1euKM+6LOZC2RsKhdSzkEbbacOKrNdhR7QMi8M+d+XKFXz1q19FOp3G0tISNjY2UKvV8N3vfhc3b95ELpfD06dPLa9d9w5TD+l0OigWi6hWqyqn9pU22IDDvS1HHQDD2M83YfPSq1evYmFhAdFoFOfPn1d9KQKBgApp4eSWD8WKedMNNmm0yf91Q0MaG1aGIXDQgT4cDmN+fh6BQEAxcqcJqSzoeRp6bLz0wh3VWLP6X/9uAH3C3Gocxw36gifs7kdX2oZ5/sMqhTLXZRxhmqZar7VaTYVyVSoVZbAx0R84qOTq9XpVeXap1LVaLZUozGNZut3j8ajw4G63i1AohGg02hdqNM5g+GgsFlO5ZVbejEHeId2Qsfubvzl2zAscB1gRJXI/kPlPVmH0OgatebvjZeTDYV4VGmsM8aeCocsVKs/Sm3VcuN1uRCIRJBIJJJNJXL58GfF4HLFYDJlMRjH+VNaj0ahqjcCmtVyXrPhKxZOeBXmvsniCvHa5P9vJWyl3dWLWyuPA3MtUKgW3261CzWTYYbPZVNWmG42GMtZkC4Dp6WllJN+6detYYyxDReV9UB7xnkkkMARcf8ZWa/MkYUXESm++VXSS1Z7PUD7mCY7j/n9cmKapPLrRaFSF2sqCR8yjbLfbqu9jp9NBPp9XlVPlmgkEAn3z+ywIcCtD3A5WhN9RYDenDcPA3NwcvvKVr2BqakpVid/Z2cF3vvMdfPvb37YdC8oI6UQwTVOlDLE3GwuWnDRGzmAD7IWGNIDkoHHRy5AONrP0er2YnZ3FzMyMYvuSySTC4TACgUCfu1hnLoF+hk2fAGT0+Dd/W5Wp5Xv8jCwtzA1YT4qWi7TRaKjQh7OEvrENWjz6QtQ3Q6tj9eOPavCNO17kfqw+O4xws3oW47j50chg2BEVEyqpJGBkjx8abFSgpOBlvg+VRRpsPJZhDtJLxzVKOTCuXl/DMFQfHxqyzMeSMnDQ562MM/m/7jmi8uj3+1X+lGzmPOrQjTaZw2aa/VWE7T5nZ3jp4zeskiPBPU2fk/o5rBSao4LkIj0AjGSJx+PIZDKIRCKIRCIIh8PPNaiVaQqSKJSQRCivWSdR9f1T71Gm70V2CplOvuqv85npz1/2aLQyBHVm/qjjTN2Ca5Xnks9YJ4ntDFMJubbtSFErwtRqv5fvW8GK4LUykK3kDaMd6CV61SDHg2MiIzxSqRRmZ2fVOuJYyh6QW1tbymhgMZtSqYRyuaz0YlmplPsmaySctpfNjszTj3lRAsFq/gQCARVVl8lk0OvtV3RkY/u9vT0UCoWBe7g+d7n+2O+u2WyeKgE+UgbbYQYBJ5hMWGdoAI2zUCgEj8eDqakpLC4uIhgMYmJiAplMpi/8wuPxIBgMKmaOTByVQNM0FZvD96WxSIUtGAw+Z3hRmNZqNdU0U25MmUwG8XhcbVAUVFSOOAkAIBKJ9HnYzuIZ6H/LjV9P5tbZ9UGwY9GszqUreFYKzWFeqVGGft9WitpxFCm7Y6zG3ioxf5zg9XoxOTmp+qbQWKtUKqppJYUvixR0Oh1VOZJrkoy9VLa5eXk8HktvHBXUTqejlFHZG5DHAOMxH12u/fLpExMTiMViaLfbKBaLfQ2Irda4rtxaEVw8Tso6wzCUcp9Op7GwsAAAyOVyyGazIx3uJO9Z3le73VbhMJ1Op69M/mHQlRcr+cC/5fl0BViOuSQeJLtupyzr5z4KPB4PFhcXcfHiRcRiMVy5cgXT09Pw+XyIxWKK1KDyKecJ92MAfSHKUmmVe68cb5lSoI+PVLz0UvD6M5SyUO4/egsH7vutVqvv2cv3abhLYgeACjllqPVRQb3H5/P15cZRSeR1yJw2+fyt9m0rQ87OyLPanwl937L6Lj5XGQ5N3YnvM7KI98vnyGfIasDUu04DVobTaX2H/F+G2NH49/v9KkT9zTffxHvvvady09gnlGtrb28P3/ve97C5uanGkuuETd7T6TQikYiaSywyVCqVVO7VacNufsj3T+p7JKampvCNb3wDc3NziMfjqFarqFar+PDDD/GDH/wA1WoVm5ubloakFfnCsW82mygUCtjd3UUulzvVhuMjZbAdBg6Sx+NR1aD8fr+ahHJzWFxcxNWrVxEOh5FMJpFKpQAc5GUB/QKKwoTnl0KEx/BhSSEuG2hKAU0hzZ4MDIUgK8/QD05embQoN1KWMm42m6eW63HYAhlkRFgxGbrioL836HU7g9Hq8+NurFmNxUkr+VYG8asCt9uNcDiMeDyOTqejqjKxpwoLLnBtMrzK7Xaj0Wgog40lzhl6xfXIPCSXy9XHmnGzpfJJRcw0TcsNb9B6GBVQcaZMlSXhrTwG+meHOT/Qv1lTJjIUk5voOMxTfQ1zzugl4V8kvPCwcdAVH6s5ZuVh069bvjYM8WYF5orPzc0hkUjg4sWLmJ2d7Qt5leeVRX30XEmra7LyGsmcKF2hkvdmRzToz0d/ptzLZYihbnxYKfW8Nmlw8j5lCNtxxpgeaelho9Fjmv1huFaVqOV42L0ux84Odnu+1XyU5yTpy/Hk65IUtnoOvDeGnb5onuWg+zpt+WO1zvT5yHumUyAUCmFqagqXL1+Gz+dDLpdDsVjsG6N6vY6nT59iZWXF8nu5T3Ec6eSoVqt9Xraz3qvO6vsikQguXbqEpaUllUrBMfv000+HDmOUz4pzuF6vq1zVz4WHjYwrGZTJyUlVnpeLk0mX9JQxtyQej6skeRpCmUymr38KrV4rg00KfjbPAw6MJSp7ksUC+gWQrIRFJY9J1rwHGmyMc6eg5Q+vTVbS4oKlN/BlgOMaCATUdfH3UQywo8CK7ZPvnYVgPQ1YbUZn/b36z7iBygpzOGKxmDKUZJ8h6aXpdruqAIPc+GUZcKtwPHrjZDiyYRgq/t80TcRiMczNzaFWq2FjY+M5wT/qxhqwrwxGo1FMTEyoXj7EUQwyu/d0RY6J2sC+4TY3N6cMaTLE4wTKchlGahXiLn9bGbH6/nIYeIwe/cDvlaGG7Xa7L3pDv/7jhurx+wuFAtbW1lAsFuH1elEoFPpIVBobVqGB0vslvYHyXvQxY7iyNKZ0mcZzSQ+bNAKl0SC98bqxZSev9XxAwiq8k/s8vddbW1vY29sb2qvhdrsRi8VUZTt9zCSpzL/lPcmG3Vb7q5XhOazsGnauymsBntcTZC6i9MRKj+xp7ltnIavtyBPd6wzs99s7f/68yvPM5/Pwer3odrsIBAJotVp49OgRarUalpeXUavVjnQtkoDQW9mcNqjT03NKT2K5XEYul1Nk4bB7gdW89vl8KmqENSv8fj9yuRwePnyIcrmMzc3Ngfc9aF1wb5P5a6c5hiNjsLndbqTTaaRSKUxOTuKDDz7AuXPnVF4FDR6y4XwwNIbcbndfKIKeQE0lihNACmD5uVKphEKhAK/XiwsXLqikxJ2dHdRqtT5mhww+lcdwONw3aVwuF1qtFiKRiNqs+F6pVFIeNb3XmwwZ4P23223VEPSswbAwhkpxLCXzOchwO6oQ1BlA+bzkMcdVLl42DhszO1h5M4eBVFD0HNDjnvOsoG/oNJZCoRASiQQymQwmJydV2HGz2VR5bcBBkQImX1er1T6Djedxu92q6AgAFWZixcQDUF63Xq+HqakphMNhVQa4UCj0XfM4wOPxYHJyEpcuXUI6nUYwGFTvHWbgWymzulGiy4F2u41SqYRarQa/34/r16/j3LlzqNVquHv3rgorHTXYEUgAVA4lST9pENh9lq/LkFMrA0AeK/+WIZHA8024ZcQIDWS7vLqjhHDq6Ha7WF9fR6lUgt/vx9raGuLxOKLRKObn5xEKhRCLxZBKpZQnl/lsJDL16BV5HXbeXQAqFULmnvNY3XDV9yz9fDRsZYVkSarqBo4eJaM/D/kZhqk1Gg2sr6/j3r17yOfzQzcq9/l8mJqawuTkJCYnJ5VSL6+H98rv4/vUM+jtpOy3CpccNP9eBHIMJXEgv49RRHIeyrYUZ0E0npXRJu9HjgXns9frxcLCAt577z0kk0m0222sra0pXTmRSGB7exvf//73cefOHTQaDZRKpaG+F0CfDsDokrMkylhwKBKJIJVK4dq1a8hkMlheXsaPf/xjlEolVSXWzsjlPfE1PbQ6FArhzTffxNLSEjKZDKanpxEOh7G9vY0/+7M/QzabRaVSsQxjtJO/Vs+uUqkgl8upomSnhUMNNsMwFgD8SwBTAEwAv2ea5v/DMIwUgD8EcB7ACoBfN00zf9wLcbn2e0uwutTs7CzOnTunEtLp5aHRo29WhmH0ld2WFYmAgzhoK7e7ZHGYA8OcFbIZdiypnCwytIP3JAsc6LkyZNt0g0QaKy+6kZ4EuJEyWVXeoxWbqeMwAWtn1Fkx0MOcbxxwEvdgpTQe5bvHaRw5R7geaIj5fD54vV6VM6TnWtE73el0lJdcGmwMN9HXGPNj7SC98vQasKDJcUiKlw3K33A4bKv8DoLdWpXv60ohZaDb7VY5vaFQaGzmpA7uIXoEB9+zOl7/baU8282nw5hfSdRQOef6sAsbPC4JZpqmYpe5JqlshUIh1duUvU65Bqks0rDVvWD6vcu1zb9lHo+ukPJYK4ON59DvQ9cdpGdIf18+O6tcTxlmzeNYoIDl/IdV8LhGGYmkG7SDjFp5jNX7+hw8DMeRb/p3SJ2M121lrOvfN67ywQ5WY89Qffb5SyaTyOfzKJVKaq+hzpvL5bC+vn6s75Zy+0U9Q1ZySneuEDRMSb4yf3p6ehqlUgmhUEilMBzl+/W54fF4kEgkMDU1hXg8rlr8yGIjLwJ+Hx0ZMq/2NDCMh60D4L80TfNjwzCiAH5sGMa3APx9AN82TfMfGYbxuwB+F8B/dZyLMIx91+jly5dx/fp1pFIpzM/PI5lM9nmbZPiCdJVKS1ffjDgR+eAlU0MGXSZCb25uYnV1VeWmtNttFZY4MzPTN7FlrHixWESpVFJGH4U0j5W5MmTw5CagTzZ+jsfREH0ZsGOGB+Vo6AaufO2w7+G5Byky0vMhv2uchLnO1vI1fS4Mc1+HbdYS0uiRHtNRhK6wulwuJJNJTE9PIxqNotvtIp/P91WEY4ERwzBUOKNpmqrsMZU7PYeC78vxkaSKzJ1gyXHTNNXrzWYT0WgU8XhclfQel9A+l2u/6MjU1BSi0ajyylix2nZK9DDEDMdcGhE+n09V72JC/LiD9zooX0k3PORrAJ6TCfzbaq1z75PPhR42Rnqw1HwkElFMtJ1sf5H7JuPcarVQq9VQq9VUzgznlgzzZ5NsVkRm+CSLK8iqynqeD8dZGjC8H6uCG5JokX/LkDCpeDGvR3rbeB792cn8H77GPBlJEtdqNdy6dQsbGxtoNBpHKvSge/p4v/I1SW7xWjh+Mp9QPns7A1Z/tnJ8hzneiujWcw9lLqMs808ZoR/3KkEnZwAgHA7jtddeU8XyTNNErVaD2+1GMplEp9PB8vIyPvnkE+Tzeezu7h75O6nfBgIBJet1osIOh8l53kc8Hse5c+eUE2ZychIej0eRGC7Xfu/PQCCARCKBy5cvq4bz8XgcpVIJn376KT7++OPn1ogVkcV1zHkeDAYRj8cxNzeHS5cuoVar4ebNm6jVarh9+7aKOJA2g1W+r4R0HMnq03t7eyrEWRqZVnrwi+BQg800zU0Am8/+LhuGcQfAHIBfA/CLzw77fQB/iWMYbHz4gUAA165dwze+8Q0Eg0HV3PrZ9z6nvFNB0wWHVAqkAmc1ESkguJlQCXv06JEKXygWi0ilUnj77bcxNzenPHCymTSVxmq1ilarpUKjAoEAksmkesCsNCmNTemWlvehC7Z6vX7qYUKDJpXcFAg51nJ85WI+igElx0APh9C/mwa8FUM66tA3Mn0D1o89CgZtpFKpZogugJFXkOW4MD/14sWLSvhns9nn1r0U8PSSt1otlMtlxVzK50BFrVwuK08PSRxWz2LuHJlNGrr0SnU6HSQSCaRSKVX0ZFwMNioDc3Nzynupe4vsNurDjDX5vlyzPLff78f09DQajQbi8fhYkS52kKH6UjkmrDxFg2DFwsvzSaWX55TX0Gg0kM/n0Ww2lSfV6jzDXs8gkMDkuUk6ylBEerTZZJttdlKpVF/IM8PDZDsAOYd0skAq+PV6XXn9SJLK4kQkVbjG2YORRhRf5z5sxZ7LIh+8HvnMmNtCfYL7mSxOM6whwmOtooekAaSvRxlRIHPteU6rPUO+pp/PSg+wmkMAlAwlySX1Gt4HZSSNbknE8tyHKdLjDP2+otEo3nzzTVy+fFkRqpVKRRXYqlar+MEPfoC/+qu/OjLhKucIq23S0z3MXkW5YkUq87x8VslkEu+++y5mZmZw6dIl3LhxA8FgUJE4PB+JVZJ2r7/+Ot555x1Uq1X84R/+IW7dumVJaujjxvnE+hHsl3jhwgVcvXoVd+7cwY9+9CM8fvy4LxSZMgXobwVitR5I5HKukujd3d3F06dPkc/nT7XS5pFy2AzDOA/gbQAfAph6ZswBwBb2QyatPvPbAH7b7pwUIhzgWCymYtqtBIE4b99v/i0XuQyRsPJk6OeT+WOsmlMqlVQlnVqtpkp3S4ONyh6ZOSsjw+oa9GuW92P1OblYTlp4HXY+fdyOuqnrRopk9eyOt1NS7ITGUe5nFDDs+B1XebIzZjluL1Jk4GVBsmfSqLdiZXWlzoqJlrAKddJft1uDUkH2er1ot9tjN67cjPSwzkHs+zDMvN1neH4SZ5TZozpuVvLbClZK7llcm/ytX4M0WqzYafmZk7gOrjPd0JFGBJ8788+bzaZStqj0yP6H0gCV1yyvXTfYaITxHLrBxjBpkjK6h42/ZZl0CWmwWa0Fetg4DnpkzTDzSY6tfh753qDzSLll9bx4jJ0BZ3Ut+joe9N36MVby12rNHHWMThNWBIs0Yl/EA8h9jXnVoVBI7XGycjFzZBlSe5zvlPv/acpbVnGOxWIq3zwYDCpnhnymMtQXOAiZZPsKrtNh7zcQCKgcWqYidbtdVKtVFItF1cz+qKCNoBOPrVZLyZvTnKtDG2yGYUQA/E8A/gvTNEvaojINw7C8StM0fw/A7z07R98xLpcLU1NTmJ2dRTqdVv0RACjDyBCsrPSqMGxCu8Y+RZ4uSxpT3BRarVZfUZJ2u60s/nPnzuFXfuVXUK1Wsby8jHv37iEQCGB7exuxWKwvzEQKGLLSDNmi+5f5IDKkU3rUZLWsQVWfmG8Ti8XUJnCa/R4k9A1LCupB+Rq68Ldi9ST0vAoro5XH0Z1P5XJcoM9j/ec0FrsVWWAleMYBLtd+NcPJyUmYpolyuayEpL6uaIRIT4c8j50CI3/k3Kdnjsn+/F4qeTyGXqK9vb2zG5gXBL2KzCUD+pn0w8iVYZV9qehw/GmAt1otFaLDCoCjoKgBB54i7h1265egMaLnVcnzHQVWyqI8l5yvVp9rNpsqZJ9FrhjtIY89riwwzYOcRPlsZVl/CUmw9Hr7DWy558uy43Ic9f1dntPquiVpKg0k6Y3Sy/Nzf6NuISsj2z1vfkYqwpwfzWZTefEoR+T90us/DCvfarWws7ODer2OxcVFy4gbfS7Ie5IKJhXYQV4VOd56JBDPa+WtO+y5WI0ncCCThw1PO0vQmJKVyGXV02KxiHw+f+yICqYEzc7OIpVKIRaLqXzQdDqNXq+Hhw8f4u7du6hUKlheXrYcm0HyQb7P1lg0koZd95xXVsfrehtbbk1PT6vWW1xPVtdMIoKfNQxDFcIqFovY2dlBPp8/9Bp9Ph+uXbuG9957T3nmHz16hKdPn2J3d1d5wSgrZaTeICOOcoERNbQdmA+3tram7IvTwlAGm2EYXuwba/+DaZr/6tnL24ZhzJimuWkYxgyAI2fvGYaBdDqNy5cvI51Oq/wJxoTW63UVssgCHhSE0o0pz8f3fT6fstbL5bLacOkdAw4EOoWpYRiYnZ1VE2R9fR2PHz8GADx48EAZTSx7LZOdp6enFYOQSCSQSCSeYzhlWwBpVFqFpHFRyE2ARVncbnff5D5tcGOTVbCsrnMQ7I7RPSH8PYg95Fyg4Bw3yA1Q37RO0ngatNmReBjVanx2MAxDhTrInmsAVGU+oH/zPyyMSkI32KTRZhgHzZ7b7TYqlYpa08xz8/v9SoaNeqiphGHsh8hEo1GYpqnkpK6ADVISjmKwSXaXIXpyTcvqe6MCGWpL2c2/9fuX3iAp9/ViFvK3/NtO6aXs0D8jDRN5TUSr1UKlUlFGGg0l/TtfxGjjHiH3aqng6vdCkPCQHi2Zz2TnvbCLNpFKqSRJ5Z6ry2BeK+UFv5vnkHqIJFflnqgbqVToSOzIXDiy/NVqVRlvh4EFJmTZc+B5QtOKoJNjr8s1OWY69HEi5HfxGeiv6d9r95qVzOZ59CillwHOJep89PpQ1nM+FYvFYxtsPp8PFy5cwI0bN5SeZ5qmapJumiZyuRy+973vKc+a1by32nP0OSD1yeOQ3sPKZa/XqzxriURC9fjUyRGSB3Sq+Hw+VQgsk8lgcXFR2QOFQuHQ7/Z6vbhy5Qq+9rWvwTRNbG1t4enTp9jY2MDe3p4K15b3M+xzk/m3LpdL2RTMYeP5TgvDVIk0APwzAHdM0/zvxFv/FsBvAvhHz37/yXEuQFeMZAypVRIxFSE7Ic3PkM0CoKoy6i5VuRFQ6PJ/l8uFeDyOmZmZvo2NLDQrYXERR6NRNSFlOwG7hTDooUohKO+PQkMqpmeJwyaivGY7xc7q9aMcS0jmb5wwyDg77J6PC31TtRLgow5u5GS+9TBFoH+Dt1IudENNP8ZqXAaRDJIskHLDyqM3qpBKLBVTq7DQYWE1x/g98rkwV4ivhcNhAFDMr/RejgLs5hPw/N4hPyN/hpGfci7r32t3Pfp1WX2exqb0gtl9/4vOWyuyY9jj5ef0sEErco9/D2N06nuTbmTI16RCLGWNbrDpXmiuJV6rrBJJg5aeWoZpDrvOSG4Eg0FVwMxuDHmt0mCV3kT5W167/rp+brv39escNIcG6Ql2RuHLNNqoe7FnGHsAy3YU0qg9yjXKKAP2FZXRQ51OB7u7u6o2AovgvCi4TzG3cdg1z+gmacgDUGHEQL/soVNDfodcXzyWuj2JUcqpYDCootUOq4TJ8Eu2DwmHw6qQ2O7uLgqFwgsT1JIABvCcjLKC3bo6DobxsH0ZwN8DcNMwjE+evfZfY99Q+yPDMH4LwBMAv36cCyDz5/P5UCqVVPd2MgDAQTiK3Gz0PkkUlGTBGObY6/VQLpdV3zOgv7ojzy8LlDBs4a233sKFCxfQarVQLBZViWJ62GRfOBpwdm59q5w2fdLLTUn/7XLtV9TJZDIol8uqq/pZ4bBJSQwSWHaGyqBzye+WG6dVArW81lHGoJDIk1Dy9efEOaQnrI+DQUHQC0PFnl5z0zSVkSHLZwPP5xfoXnqgP8RDznFuaFK55XeQiQ+HwyqvgB4CXif7vY06uKGyvUE4HFayk5vnsBu6XKd2bDp/qtUqtra2UKvVMDk5iWAwCMMwEAqFMDMzg2AwqDbZlwnKXn0MeB/MXwDQp3zLz0lDzko+SuP/qEadrtTK1+T+0mw2USgUlBFB0lMaxVb3eRxwv5bK16Bz2hXukPu1HppnNS+5bvmavrfqY2gld03T7FM8SfJaXYM8l931ysga5tB1Oh3Vg1AvkDQIgUAACwsLyGQyqjiQvE5pFBqG0acLSX3CKo9XD23kvUmjVWKQwW/3vnzGdqG4VvN7WN3jtMAwfOp98Xi8b+x7vZ5K0ZHPYBjQy5VIJDA9PY3FxcU+Q+ru3bv4D//hPyCbzWJra2tggRFdf5Sv6XC73YhEIojH4yq8bxiEw2FMTk6q6w6FQuj1elhbW8PW1lbffPd4PMqIonwH0Ndih6SFNNj8fj9arRZcLhdmZ2fxjW98A7lcDnt7e7h//77l/bhcLly5cgU/93M/h1QqhXfeeQczMzPY2dnBgwcP8MMf/lBVcn8RMGLAqkWXjpPS5ySGqRL5PQB23/q1F/lyTm7mmDG8ScatSwOKzBQAZSDRimfoBNktMllMNKxWq+pzLGksvWs60+V2u7GwsIDXX38djUYDW1tbKJfLfQYb2S7DMJRgpwInH6KV4JELy4rl0yFDIrvdrrqHs8KLCkxdkB/VeNOvgcqFLmhG3VgDrFngYVn4YWHHkurEwbiADCQrx9IDQyVN3pskcazmjG5IWW1uumLG92V4JCspynh4CvRxya0kycVoAebnSbJoEHRDVyekrOY45WWhUEChUFAbNK8hHo/DNM3nQldeBuRcslufUvbbfW4YIosY9rjDjpefYa5FIBA4tZBICanADArd5/4tc7l1I4vHyL3eymAbRH7pMs/OQADQZ/xQsdS/w8p4k/csDTauCyp3nU4H5XJ56IbZBCtqTk1NIZlMPteaQRJyJJw4biSUpMFmNw76XJVGPf9/EYPNymCUn9cNjpcNw9gPF2dBvFgshkAgoAwP9hHkz7BRAZzPNHzi8ThSqVRfSG69XsfNmzexuro69PUOMtbknun3+xEKhY6Uw+bz+VR4Iw0+EhBsL8DnaxV2ybnEdcBwYBkOzfXmdrtVD7Xd3V2k02nb6zQMA9PT03j33XeRTqdx7tw5xONxFAoFbG9v4+7du6oq7IuApK9s0H2W+tTZav0WoBEmBSMfHvC8sGBIFIWhYRiq5xKNNeYNSAEmBa2e3yIngdwQ5CTy+Xyq4AAnHgU6LW1+p6wUyc9L5kEuJNk0lPcljRE5wRk/TZb/LKFvbMfZ1PWNwE5RsVJwrNhgK+/aqMNOoZCwYuCPA32sdYPELn9y1MBrJbPFdc73qHzKNc57s7pv/fegY3UlgoaiVBj1/BhJOMkeN6MGGp3cfKWRybU2SAHjaxxv3RAYNL8Nw+hTGPTQoGazObBx+cuGnHdUOHQPm5UxYEd8DbPGrZQv+bcVIcj52Ol0UKvVEAwGh847PgtIuS7/5v7LY/iaXhFWvwc7o8Fq3HVjxSoqRpIx+vH6Nchx118DoLxrR/HASDD1Ip1Oq9wp+R06aUXUajU8ffoUlUpFfd6qYJscK7sxs5q7Uh+wMsIIaWgTsiy9lANSEbb6Tkk0HaV64HFgmvsVR4vFoqoAzNQU6n2y1dOwyrvL5UImk8HCwgLS6TQymYySfZubm6hWq9jY2DhSEQvpCDjsu2ko0hM77DXzc1NTU1hYWACw/+wymUzfmFy9ehXJZBKBQKDPkJVjJKtUkoyVxVxoHEUiEUxPT+PixYuqgFKz2UQoFEI6nUYwGMSFCxeQTqcV4VcoFFAsFlVLrKOEH9uB847EJtfy58Zg4+B7PB60Wi0ldNh0VipfZGBpINHVz4VCVkkX2DSKpHImSwpLASsNAW5ywH5vjFgs1icc6RXk3xQ8PJ8sISw9G7oCx0VGY5FJl/LaGI45PT0Nr9eriqGcFaw2QqsNfxi2d1hFQd/05LjJJOBRUDyGhZVBAViHMfJ4icOMZbvxl94heqIZxjEORpvf71ehFRSWQP94yjUucwukIqV7zchw8jPyOUhSx+VyKSbR7d4vVxyNRtU5eCzlE0MM2dPptMv9HgeGYSAajWJmZgZTU1Mq3BToDyMdxKZLWSojHAB7Ly+wv/ExtCgWi6l5GIlEMDExAbfbjY2NjaGUj9OGbggBB/NOssSsBmhHyujnOAx2SvQw18pnR6W22Wwil8spgtMqBE7/zrOCDOOzug5977E7TofV+A06xs64HmRgDyOfrc53HIPN7/djfn4eV65csQyJpHyXRdUAIJvN4jvf+Q5WV1dx48YNfOUrX0E4HO6Tj1Sy5f3q0Rh6aK8OK2NBl7dcNzQS8vk8stmsqrBLGS4JdyuwWh+jso7Si+yo6Ha72NvbQ7lc7tMhJYEs20IMu77dbjfeeOMNfO1rX0M8HselS5cwMTGB1dVVfPjhh3j48CE2NzdRqVSGvtbD5Avf93g8SCQSmJiYUNXPhwEjIJLJJG7cuIFf+IVfgN/vx97eHgqFggpz7Ha7SCaTuHjxoiqtz3kp61RQz2X4pHTIcIw4X774xS+i0+mgUCjg9u3b2N7exvnz5/HBBx8gk8lgaWkJr7/+OrxeL7LZLJaXl7G2toa9vT3UarVjrzuCBGckEkEkElGh5pLU0cda3stJ4KUabBxAJkPLxorMWdOru9EVKRN5aT27XC4l+CVjBvRXidIZOt3Txt8UgGT3PR6Pul6+Rw8a81iAAwWu3W6rGF3dw8aFLWPkqbSYpqmaGepGZygUemn5MYdNvKMYa8edxLoyPU7GGmHHDgOHC9zD7ldumFbKrs6+j4OxBvQzW1wjgDVLLn84Brrng5BeCGncyWP5Q8adLKNegY5yxDTNvhAZypaXbXhYQZYp1smPYYwLqzGXz0eHXL8MxeEzNYx9D1sgEFDl/UcVnBNUdq2q9upz7Sjnln/beTnszm2lMHOvYiSL1bW9LFk6rDH6eQeJIoak6eHaVsQCANTrdWxsbGB5eRlzc3PPySepj+iftTOYDpNnUnfR5ShlOWWu1JOk3mZ3P8BBaBr79J0mdEKe92R13FHgdruRTCaxuLiIWCymvFGmaWJnZwcrKysolUonXsnZNA/CFUmAHsXDRg9YKpXC4uIiQqEQJicnUa1W0ev1lIxhFU25Z8u5xrnAyBkWXLHagzudDiYmJnDu3DmEQiFsbGygXC4jlUrh/PnzmJmZwezsrKrOvrOzg1KppDxxJ1VRnUS3DIk8zMN2FJLuMLz0HbHRaKjE8q2tLayursLn8yEajaoS0/yRFXLIaOiDIRUIvZSw9GJJhsRuA5QsMw0mmVMnH5bMWaPgkV4/WdiAk4cGG41NoD9sgAUB6FFgCVHGo78sHLbRD6PkDRJ4UhmS55dz4bjK0CjAyti0Y/FfFHIcJSMYCATU3B9lxZjQyRbel1yD0ljSc0usFBLOI+lp5xhJgoWbCL0ppmmq0s5k/2jsSM8GSz+PYjgkcJBMPzs7i4mJCfh8PsUY6uHbuoImjVwrxUy+rocs8TyyXQtBBpflnUcdzFmmJ/UwQ9WKsJKyzMrbJV+z2vw55/Vxl0otr1HuHXYydFzl6ucB0qChES5lnk5QAftRTNvb21hdXcXy8jLu37+Pvb09JJNJJJNJJbeA5yOM9FZE8jp0Q9vKQ6mvb2BfHheLRfR6Payvr+P+/fuqB+P8/PxzxJnVvhiJRDA7Owufz4fNzU0ll08bkUgES0tLSKfTSnk3jP2emzs7O6qa46DiFsxDjEajuHTpEubm5uDz+VCv11EqlbC+vo5sNotCoYB6vX6i+4eUz7LK+bCoVqtYXV1FsVjEpUuXkM1mVaQJ90mGx1PX5t7JucpIDBkBQB1bGvryeg3DwNTUFN544w1UKhXE43Hs7e1hamoKi4uLiMfjqv1Bu93GxsYGbt68iZ2dnRPNhZbXLMm6s9rjX7qHrVKpoF6vo1ar4dGjR4hGo0ilUrh27RpSqRSazSaq1aoyVmQYJA0jVn4D+gsL6InMckOTxpoVIyy/g/9b/ViFCciJJ8OFpBLEY2h4kgXlZOffVAwNw1AlSmu12pn1YNNxHEPJysAb5hw6M2fn/RgnSE+MziTq5IEuvI76PTw3Iecqc4cAjI3BZrWGZasPWTVWjq/MZdXXL+WH3FwkGQNANRdl/6NWq6XyDTwejypqRGOt2+2q0ttMTh/FeWoYB30wU6kUgsEgarVaX8SDy+XqI5s4ZhxvaRjLsZfFXXg+Oc9dLpfKWeA87PV6iq2V748KrAgjWdSKbQoAe/kkx8rquGGUTitFlkq39JKSvfb7/eoauZfK79QVJAejCbm+ACjPVK/XUwqkNHY4T6rVKpaXl3Hnzh0Eg0FMT09jYmICS0tLWFpaUkY9ZSajB2QTYOoyViSj/J+/KTPoTZNGYa1Ww+7uLur1Ou7du4cf/ehHaDQaSCQSuH79el8xFSuizTAMpFIpXL58GclkEs1mE9ls9kwMtlQqha997Wu4fv268iC5XC7cvn0bH330EYrFIu7fv49KpWKrxE9PT+P9999HOp3GF7/4RVy5cgWdTgc//elP8fjxY6ysrGBtbQ3b29t9RThOAlJ2y4Igw3rYisUi7ty5A7/fj3PnzmF1dRWpVArJZBLxeByAtQyj/s4x4XyjfAKgCC+5l/OaAeDChQtYXFxUkS66bk6HSr1ex/379/GXf/mXKJVK2Nk5aBGtz9+jguPGfnKHOVBOek6+dE2NihQNMyZ2WsUD6+y3rkhYCQ/CavPUjS6dNZJKmx1Dr2/Q8tw642nHbFpdu7xXaeSx38XLYO2lAXHYxq6zbjoG3f8w5+U5xlHBsAvn1OeArsQNe6+DlD++Jj254zKGuocN6FcO+L4+vlZ5BXJM9fBJ+TmOEZUZGQathwEC6Muj0z1QowYaq6y+yQ1vkDdIGiu8N4YDsgpXp9NRIeS6DJTfLceMr8lN/GWEfR8FnC8k9gblr1jJOLnfWHlG9GN5Hv2cw8xtXqf0mOjnH9V56uAAdvvmoGdH/apWq6FcLmNvbw8ul0u1CGKIGyMNTHM/6qLRaKgcfBLPOqx0F1n4xOp6q9UqSqUSqtUqCoUC8vl8Xz2AQZAkWzgcRqPROLQ40aC5bbd27MDcL7YiicfjcLvdyGazSCaTAPajV6y+j3IwGAwilUohk8kgGo32Kf/FYhHlclk5J04TdrJ5ELrdririUavVVGSBzHe2IpSk8a3v0yQFpS4u5w8/o4fJm+a+h4skFF/rdruo1WrI5/OoVCovNI7yfvi/TJ/43FWJJNrtNpaXl1Gr1ZBKpVCr1ZTbeGpqSpXKpjVO9oeTQSapSibK6m99UuleHMLqQeghQFavS8NMKjVS0SOrz7w0OYnZF4kTuVAooFKp4MmTJ7hz5w5qtdqRElFfFFIIAzhUkRqktBwVVgJfPjP9/KOudLjdbgSDQYTD4eean+rzD3jxe9LnOoA+horFfUYdLA4iGUHeR61WQyAQQDAYVO9JwU5jAjgwqKShR4ZZ9mijTKFiIMMFa7Ua6vU6Go2GKu3PtUsjhQaQbmCOGgKBAOLxOOLxuMpn4PVTbjLshwo/Gf1wOIxyuYzPPvsMm5ubKJfL2NraQqPRwJtvvolf/MVfRCQSUefo9Xp9zVEJjj2wH3KUSqXQ7XaV4nNWm+FRwOunckAlS64x3ifDEWk06etxGCXcav7oigSwP9dZnZSKt8fjQafT6fOwWZ3nOAqcg5cDGe7KlArKMzuYpont7W386Ec/QigUwu3bt1WBH0kw0XMuQy6ZnmFHMsq5KJVYPZIE2PeklMtltFot7Ozs4MmTJzCM/bBCnYiWupk8dzQaxfnz5xGJRPDw4cOB9x0OhxGPx1Wkk9QZeT5ZGE6vNQD0E/Y8xuVyqQioc+fOoVarKQN0dXVVeUApyxjFcOXKFbz55ptIp9MIhULY2tpCsVjEJ598gh/+8IfI5/On1tJEjqV0fBxHxvI5Mp+NMpyknf6djJjQn6nsn2rn+KBxRvkpa0Dobb/K5TIKhQKy2axtQZrD7leS2VKW69VJqQtQh5JEpJw/J4WRMdharRYePXqElZUVFZ+ayWRw4cIFfOlLX+pr7grsu9UZPsgJom92ksG1KsEtJ4cVg2kVSqkbf3YGnG5UyAVPJRFAX0NBfi4QCKgqNPl8Hnt7e8jn81hZWcGdO3f6quycFWQlr8M2BSuGXirI8j07L5DVeawMNjvDbVRBgy0SifQxcXbjaqWU2UGfR/J1XZFk5cKXnQ85DAzDeM5g49pvtVqo1+tqXTKZnQYbw/EoH/TcNgB9sfwkI7gBeL1eVRWqWCwqZpEbAec1ZQKvLRgMPhcyOGoga0mDTZZ4lkVIdGWJymIoFEI+n8fHH3+Mjz76CLlcDvfu3UOlUsFv/MZv4Mtf/rIaV8o/qwRw2VMzEokgnU4rJWfUoK8rhkSWSiVLgw2A8h7Ivl5Wm7mV3LPaW6y8Y7IXKQsXmKapiMFOp6PKusvxl99n5/l3MDqQc4Nyi43Jh3lu29vbqlqo3fPm/zK0XFbotttn+Lee268XBdG9Lgwhz+Vyz6WY8DrluQ3DUAZbNBpVhSbsEA6HVZ6YJPYpi2Q1b1lUjnuA/G5pJDAHOBwOwzRNBINBFAoFPHz4EDdv3lRrnrJsdnYW8Xgcr732Gt566y2kUink83lsbm5ie3sbH3/8Mb773e+qSKrTgpXBdlSYptlnsDHnmPfbaDT65hereuvOEc4POdfs9Dm9RRavm3OJKUOVSkVVH9UrOB6FALfSFWTxMxpskvSWht5ptJsYGYMNQN/Dpqs+n8+rhRwOh/uqQXIwdVe9dLtKIws4YNiB5/MJ+Fn5cHSjQG5wVvlrdsJMd/lK5oBWvGwcSOOGE49eNjLUZwkrA8jO4BpmMeifHWS8Dfq+cTPWJA4z1KyOO6nvlQb0qBtrBAWhLtQlIyqVEGkkyfuUn7PLj7DyqkvBTTk1iJ2U1zvKCjAVFL3flCS89Lmqv0+vIzfLSqXS169Ozjn5N6HLVvlsRh06U6/fszzOjoji+3JchlHAdUJMnhvoz+fmHmN1jYOMQQejg263q5TRaDTaR6AOIjAlGJUwDGTEAL15h+25cv3yu4YJSaOncFDVPV1GDysnXK6DIlBynVDp7nQ6fX9LPYwGG6+NRDqPazabKt+Px7jdbkXGut1utNttJBIJ9RMKhZQRWqvVkMvlkMvlUKlUVJ7Xae7LHLtms9lXF+KoYEsuv9+v5pQe6qg7PKx0aV6P3NcH7Q92crTVaqFYLKpiLXYG01Hu1Uo+yig9u957pyVHR8Jg0x9So9FQlXK2trbw5MkTBAIBTExMYHp6GsFgEEtLSzh37pwKzZHhhFaKBZUL+bfVpJChlnydzCiZLJnXpm+AdqC7Vip88nrq9Tq2t7dRrVb7ckG2t7exubmJWq2GlZWVkVCwrZQu/X153DAbybDfq5933KC78aUAIOyMtMO8bXIuys/wc1TeuAnJBu+jDnp0QqGQWuNk1RgSSS8cFVXTNNWGxEILVF75GmPwqdAC9soySRV6J/UwN/7N50BPOdnGUUOv10Mul8PDhw+RTqcxNTWFmZkZWyVIhktJGUrZpof76WyoblBI5ppen3K5jJ2dHVWUYBQg54M+L+Sc4D1IkpC/qfhSyZYhZjoJaBWJIK9Fvy7KFHp66Q3h/GOYpIySYL6iVKxkka5xlK2vOorFIv7zf/7PePToEd577z289tprSCaTSu+RSq8elnUchZxrWxLKg+aFXPtybQ/7XawYzgqDerQDX3O5XKhWq1hfX0cul0O5XB54f+z1GAqFEAgEVHVf+R2SyJMF4WQUBUMxA4GAKrffbDaVY2FnZwe1Wg3dbheLi4twuVyqd2gsFsPCwgIikQgymQxyuRzy+Tx++tOf4qc//SkKhQKePHmilH8+x5M23ihrK5UK1tbW0G63sb29feQ8L9M0sbGxgQ8//BCZTAaTk5O4evWqSvFhhV85D/mMZGi97KEs71tvmUNwbkvdieHfGxsb+P73v4+9vb3ndOVhnAI6rOa6JMFktI2c56epn46Ewaaj3W5jd3cXwP7N3717F4ZhYG5uDufPn0c8HlcucZ/Ph3A4rJJm9f5IPIdusNkx67VaDbVarc8Txg1ZCj+pqEh2X4fciLkYZU4NH2ypVMLq6iry+TzK5TJ2d3fRaDSwtbWlFtYoMM5HZX4Pm7x2hp8dE61fx7gpFpxDUjDreZWA9TjrXiKrc/NHzkWeVwq3QezQKEJWZ5IVpLhpMjGeOWwU6kB/o04aclx/NNyowB6mkDCEhpu5YRjPhc1QRvj9frV5jeI8NU0TpVIJa2traDabKJfLA9ltbqQ6E0olQDfYdLbdzmCTxzAPpFgsjlyzcSsjSspzOXZyrGS/KxIGPp9PMe1SKbPyBltdA/+mUkdCURqINNqYT2lFFEoFX4b7Oxg9VKtVfPbZZypMu1qtAoDlurQipY+K4xheL/JdrVYLlUoFLpdL5Xjr8oPztNFoIJvNIpvNqqbIViA5Eg6HEYlEEIvFEIvF1GssbkS9Ue7NMvdU7qterxfVahX1el01087lcqqsf6/Xw/T0NAKBAM6dO4d0Oo1wOIzp6WmEQiG0220VQn3nzh384Ac/QLVaRS6X6yNUdM/SSYwxjaR6vY6dnR10u13k8/kjP1/TNJHL5XD79m1kMhl8+ctfBnDQX5MGG+9BOixkiX8Zosrz6nLWSq+m7OOez+v55JNPkM1msbm5eai+NAiD9FWuNY4jw9wP++xJYCQNNglOVsMwlAvWNE3Vv8Pn86lkeXrbyALL4hg8B3NMOHH0OFgW9JCvM2GbccWSKeDEO8x4kEJHMsr8TLVaxfb2NorFImq1GorFIlqtloqPfZmKi1RKaOzqzLB+vG7Ekv2Tx+i/dWNjkKAaR2MN2B+HRqOhGHZpTFkVc5FCe5BX0+p16WHWN3XpXRolpdgO8vqlIaDfl/yxm0fyWIZXyxw3AErp5Q9Dghi/LktP65DPdJRz2Cjvcrkc3G43Wq1Wn2Ehja5Ba9HqPTtCQB4rx5p/1+t17O7uIpfLjYSHTc4nneQD7J9/vV5HoVCA2+3uy3Pg5s69R85nOd52Hjb5mvQK0MPncrkUy0/2l1XdrK5TJ4kcjC6oOwBQuVKmaaoiVlLe9Ho91WqkXC6fSBsgnag4aZA0YgijLHxExb5arcI0Tezt7al8vFqtZntO09zPmy0UCipnu16vqzxj5uwy91l+TrZ14r3LPGkSoABUlIY8r9/vV4an2+1WMoDV0Ov1urp+vVCHrhedFChn3G43EomEqlR51Iq8JD1ptK6srOAnP/lJ3/4oG2cDB3u43DulPqnXhgBguX/q+icJW+YSs1n2i8BuzGljMMfbKu9OGp0njZEw2IaZkKZpqkXn8Xiwvb2N733ve32hHKzEQwufccT0kLndbsWqtNttVCqVPs9Zr9dDqVRSTR3ld1uVbebnCKuHZLW5y89JBqJWqynGn2zEKBSFoAu9VCqpMrRcMHJMdCEjFyP/lu8TUhHSP2unHElDZJzQbDaxs7ODQqGAXq+HycnJ57xGkgnXWTbplZAYJGAYAiLPX61WVVLuaZcPPgmQ2WSxHno0WIyI9wgcsKqSHZaeBemJZ1Nhno9CmEpQKBRCOBxGOBxWPSJN01SVXaVHBXi+KID+bEcJvV4PGxsbKJfLmJ2dxVe/+lWV5wHsezUZni2Zbv1erAwZso+1Wk0ZvTyWITAca44TQ8A//PBD5PN5bG9vjwSZoIceWuUvyjHpdrvY3t7G7du31XzlPkTjSYZJ6gaxJACtDDd9rsn9KRQKqeqla2trWFtbQ6lU6gsbk89SzwMaVyLs8wAa5oZh4Pbt2/in//SfIpVK4f3338cv/dIvIRKJKAOk0WhgbW0N+XweDx8+HGjU6LCaa3JeDFqTgwisQZ/r9XrY29vDw4cPkUwm4fP5kEqllDfaMAzUajVsbW2hXq+rAh3MWRqkI+VyOXz66ad9JJxO9PE3STl6irivUI7Tu8lr4p7D9wKBgNovPB6PColkmk+j0cDm5qZ6Jmtra9jZ2emLupKG+UmC52232wiHw7h+/TquXLmCSqWCv/iLvzjy+dgPOJ/P4w//8A/xne98B6FQCHNzc0gkEpiZmcE777yjjMJUKqUMXukQIazk3WHRRNVqFbu7u6hWq3j69CkeP36Mvb29vgJQx4W+rxmGgVgshvn5eYTDYZU+JaP6ThsjYbANCzIkAJDNZp97PxQKYWJiQjWtZcgJXdtut1vFMrdaLeTzeaWMcAMeRgB83sCNotlsIhgM2hpb8n9+TrLAVoqG1TmGGfvDFvSogonjbrcbyWRS3av0Hh3Gqg2zARJkD6XBbBgHTdhfRsXR40CGlgEHFbuo8MsQEp0gkAopGTt65JnTR8WV38GNW/72+/2qUi1ZQ36fNN4kY0hv/yjCNE2Uy2UV1kPmGTgIgZL5usOwvnLMSZTRgJafJSnD8WYlyXK5rBTNs2xdYgddUZUGjk7gSVlXLpexvb2tiEMav1aNq63WvFQG7WSdTgDKXB+v14tisYhisdhXBIaQRqJjrI0PuB53dnbw0UcfIRgMIp1O4+d+7ucQCoWU7On19tsBbW1tYW9v78iknJWhpv8elmgfFBkiQW8/ABVuThlBHY0pI5ubm1hZWUG5XB7qvMMarAxVdrvdiEajyvtEUtDn86k+k3I8mNvm9XoRi8WQTCaVl8nj8aDZbKoea2tra6q5drlcVl5DfdxOAzTavF4vpqensbi4iEwmY9lj7zCwVRWw7/G9desWwuEwrly5gsnJSVy6dAkLCwvK6yY9kicFtioplUqqpUKhUDiRc1vJZL/fj3g8rlraUAexk80njbEy2A5Dt9vtK5/MRU8vAuNOWdijWq0+52EblVyxUQIVLpaItlJCrSatZK6s3Mbyb12Ro8JN74cMZ6Xi3Gq1RjY/yA7Su1qpVLC1taUIBr3hps6yE3rMtzze6u9GowEAfSFSDJN52eG2w4Dzjw2eGaoiwxr1+UglQYaaykRubvSJRALRaLQvTJLMnWEYystmmvthbhx3evxYzIHPlX13WM2tVCr1hViPKlqtFu7fv4/vfve7iEajmJ+fRywWg2mafX3vGMYnk/WlUStDWRgWxPc4RjRmuYkXi0WlwCwvL6tqaScRxvWikEqjXditlImcX7lcDo8fP1ZyU7agkd5zGdpj9d1WMpWwIrx8Ph+i0Sg8Hk9f8RbZlNhKZsj7OCu22MHxwegbem7lPOVz9Pv9CAQCap0B/XvKoGIk+v48TDjkUV7Xz2eapgqJ9Pl8faH6nJuNRgMbGxvY29t7rgXASYE6IPNpqZCzQbfH40G1Wn2OsCOR5/F4sLW11dd/0u12q5QXFpeTMu6s9l+OsWHs10xYX19HLBZDNps9MVnb7XZRLBaVjP/oo4+wvLyMWCyGdDqt5CHnpGzTI5u30wtqlSoi05k2Njbw05/+FHt7e3j8+PHQFVCHgSTl+FMsFrGysoJAIKDIsN3dXUUu8jOSMD5JvFIGG71m0kCQgkkqdzIchLBiTR2gz0iiAsbcH4Y3sWkxYF10ROapWEFPtuczKpfLqNVqKjTC5drvfZJMJlWIqzzvUdi/lwGGLwHA7u4uarWaYrtmZmaUJ4f3SiXYium3CieQCiAFNNtBbG1tYXNzU4UDy4TqUUcwGFSNR8lK0pCjkNfnHYU9x5BCntXImE80OTnZp5R3u13s7u5ie3sbkUhEeZ9YwUyGVbLHFZnLSqWi8rCePHmiwjNGwfgYhHK5jG9961u4d+8elpaW8Hf/7t9VTXWB/nYG0mPGTTcSiaBWq6m1yFj/SCSiFB0a0CzywnCj27dv40/+5E+wtbWFO3fu9CXvv2xIw1xv80K5RtkoG4w/evQIGxsbtuvyRTBIxvGaqMgzpN6KiJSeNd4H5/Q4kWCfR7TbbdWsvdFo9JGq7IsViUQQj8eV8QAczGcAfTlaErqxdhqwWhMkMJmCIWWN2+1GuVzGJ598go2NDaysrJxKKD9Jf8p8Gh/6mtfXhyR0ZCsN/jAUkb/Pony/1b2xWMr6+jo++eQTFAoFPHjw4MT6vjWbTWxsbGBnZwfLy8u4efOmMsi4D8fjccRiMfh8PkxOTiIejyMYDCKTySAUCsHv9yMSiajCSdKbSf2J6Ux3797Fn/3Zn2Frawvlctky7/k4c5j7ufQO93o9rK6u4vvf/z7cbjf29vZULqKM+NPDzE8Sr5TBZprmWOTjjCN0o0FCMs383055toM0Qng8lWOd0dZZbXkd4wAuZBoMHo8H0WhUhS0xPMxqDOXndchNUIZtsc9WpVJBoVDo6zUzLtBDIq3yfmhU0NjX3+dPr9dTXgd6PPRQt3q9rti6arUKv9+vnpfOuvEzsnpkvV5X4RpnyaIeF51ORxlKPp9PKUxWc0SfY1ZeTjlndbkhxwzYNxZXV1exsbGh8ipHxcC18nLbhRJKpeIoYVhnCTn2uvHIZzgucvTzDEn8WckvuS7tojZe5nO2+m4WwtI9T7zWdruNYrGIvb09yzDCk4BcE+OQKnBUkHSr1+vI5/MIh8MolUondq+MiiLy+Xzf+4ZhIJVKIZFIIBAIoFarIZlMqh7L4XAYwWBQ1aogGSYNJ1llk22/Njc3T+T69XvRyQsaZy6XC7lcThUHlAUJrSIZTgqvlMHm4HRAz2U4HEa1WlU5L4xhJssuDTb+1tngQWETcqPha7VaDc1mU4VYeTwe5PN5PHnyBOVyGdlsVgkbfnbUlWNCKvqFQkEZJbxPGSZAtsmu8iDPQ4HM0Lxms9mXHExDbVzGCNgfp/X1dXz44YeqpLKsuNVutxEIBLC1taWqi4XDYQD7G0Y+n1dzg8xpNptV4dM0UmhgSTbU7/eruc/wh3a7jXw+j83NTbhcLhV+zcp8DO2hZ3McxltWlXv06BH+zb/5N/jkk0/6cjpkEZV0Oo1EIoHNzU2sr69je3tbkQHA/rjfv38f+XxeeYzZ84ebHFnSBw8e4P79+yrnapSIhE6no3rukfBgo1iv14vd3V3s7Oyo/JRRunYrdDodbG1t4cGDB32yslQqKQVkUJl0B6MF0zSxvb2Njz/+GPF4XHl2ms0mtra2UCgU8PjxY9UCQBIlL/MZW4VZSoNN9s7kdcp+lw5eDJVKBXfu3MHW1hbW19dPzMM2DBqNBkqlkqpmu7e3B5/Ph42NDfj9flW8haGRsl8lABVa3ul0sLGxcabEGPMnDcNQ3krdU32a8/NQg80wjACA7wLwPzv+j03T/IeGYVwA8AcA0gB+DODvmaZ5cgGkDkYGzWYT2Wy2r8w5X2dMtFXIozTYZIKqVQ6FVbwwcNBDi71SvF4vCoUCVldXUa1Wsbm5eWaL5aQhWdJcLqeSZWXoBUOtWCKYhpueuydbT1AZpiDRe56M0xgB+8bE8vKyEpSy6qOVl4Mhs4ZhoFgsolQqqVBaVuxiqOL29rZSXq1yA+XcpgePeQ309sn5KlnucVIwWCG3XC5jb28Py8vL8Hq9SCaTWFhYQDAYRCKRQDKZVP2FZmdnsb29jeXlZayurva1idjd3cVPf/pTJBIJlXtYq9Vw+/ZtrK+vq7LkevuSsw4TOgyUPyzikcvlVA6G2+3G7u4u1tfXVbGqUWflW60WVldXcevWLZVA7/f7kcvllNE9DoangwOsrq7iO9/5jvJMMGef4Vr5fB7FYhHAQWgz/37ZkLJRNiJmqXbgoMAUw6THRaaOMgqFAj7++GNVdOskc78GgSQ8vXC5XM4yGkOP0rI6D/WnszI2TdNUeWtAfyEuPbXqtDCMh60J4KumaVYMw/AC+J5hGH8G4P8E4B+bpvkHhmH8fwD8FoB/cmpX6uClQfa4kE1guTkA9kVH+JosHz+MwSZfYxInDTYq4fRmvArCWxohBMeUuVeymp6e2CqbODJGnl6iUQkvexHIMDm9DDkh5wHzcKrVKmq1mvIQkX0mg3uWAn/UwU2HnllgX4kKBoOq2Itpmqp0Nb2PJApknlSz2VQNWev1ujLYcrkcstksCoUCdnd3T6yi12mCZEe9XkelUlGsr8vlQqlUUnNsHHoa0pOaz+dVkSO/349KpaKUZSetYHzAELR8Pq8UYRo7zFNm1ID8jPz9sqB/P2Uxi2IxnI4GG/trjVs4/yiCudwvA5SnwPiFnb5sQtE4yqI1DCME4HsA/g8A/h2AadM0O4ZhfAnAf2Oa5jcP+fxo72YOLJFOp3Hx4kXVe4qeNFkS3iomXv4v82HsDDarjYSvM/mYIWjlclnFMlMxfNUgE65lVT2Z1CwZHhohDPOT3p5XAYOKLUjIHowkFaiYer1eFXrzKs6Zk0YgEEA8HldhKsFgUPUXikajqNVqePTokQpN5rybnp7GhQsX+voXtdtt7O7uqmIJuVxubIxlr9eL8+fPY3Z2ti8suVarYW9vD81mcyxkkd/vx/nz5zExMdFXOKdYLGJnZ0eVHy8UCi9doXdwOAzDQDKZVAWCKP+Zu0zvCcOz+Rng5Rps+jXwPpLJJILBIJaWljA3N6eOYejnvXv3VDl8p/2Sg1cAPzZN891hDx7KYDMMw439sMclAP89gP8bgP9smubSs/cXAPyZaZrXDjmPswOMIaySl4+Dk9ggZGUpnvPzolgMO/6fl/E4DFb5jOOU4zgqsMpJ1Qvc6IqTLjPkutXDnscFVrmjAPqIqHG4J6sCMXpu0zjch4N92BUQ0YnPUYdVcTEJGc7pzFEHrwiOZLANVXTENM0ugLcMw0gA+NcAXh/2CwzD+G0Avz3s8Q5GD9KF7eDlwdmgjgar8XLG8Og4TgjVqygzXnY4zEnhVbkPB/t4VYwXeR+vmuxw4OAkYN8YywKmaRYA/EcAXwKQMAyDBt88gHWbz/yeaZrvHsWKdODAgQMHDhw4cODAgQMHQxhshmFMPPOswTCMIICvA7iDfcPtbz877DcB/MkpXaMDBw4cOHDgwIEDBw4cfC4xTEjkDIDff5bH5gLwR6Zp/s+GYdwG8AeGYfy3AH4C4J+d4nU6cODAgQMHDhw4cODAwecOR6oS+cJfZhi7AKoAsmf2pQ7GGRk4c8XB4XDmiYNh4cwVB8PCmSsOhoEzTxwMC32uLJqmOTHsh8/UYAMAwzA+cvLZHAwDZ644GAbOPHEwLJy54mBYOHPFwTBw5omDYfGic+VIRUccOHDgwIEDBw4cOHDgwMHZwTHYHDhw4MCBAwcOHDhw4GBE8TIMtt97Cd/pYDzhzBUHw8CZJw6GhTNXHAwLZ644GAbOPHEwLF5orpx5DpsDBw4cOHDgwIEDBw4cOBgOTkikAwcOHDhw4MCBAwcOHIwoHIPNgQMHDhw4cODAgQMHDkYUZ2qwGYbxy4Zh3DMM46FhGL97lt/tYLRhGMaKYRg3DcP4xDCMj569ljIM41uGYTx49jv5sq/TwdnDMIx/bhjGjmEYt8RrlnPD2Mf/85mM+dQwjHde3pU7OGvYzJX/xjCM9Wey5RPDMP66eO///Gyu3DMM45sv56odnDUMw1gwDOM/GoZx2zCMzwzD+D8+e92RKw4UBswTR6Y46INhGAHDMH5oGMZPn82V/8uz1y8YhvHhsznxh4Zh+J697n/2/8Nn758/7DvOzGAzDMMN4L8H8CsArgL4DcMwrp7V9zsYC3zFNM23RJ+K3wXwbdM0LwP49rP/HXz+8C8A/LL2mt3c+BUAl5/9/DaAf3JG1+hgNPAv8PxcAYB//Ey2vGWa5p8CwLP95+8A+MKzz/y/n+1TDl59dAD8l6ZpXgXwPoDfeTYfHLniQMJungCOTHHQjyaAr5qm+SaAtwD8smEY7wP4v2J/riwByAP4rWfH/xaA/LPX//Gz4wbiLD1sPwPgoWmaj03TbAH4AwC/dobf72D88GsAfv/Z378P4H/18i7FwcuCaZrfBbCnvWw3N34NwL809/GfASQMw5g5kwt18NJhM1fs8GsA/sA0zaZpmssAHmJ/n3LwisM0zU3TND9+9ncZwB0Ac3DkigOBAfPEDo5M+ZzimWyoPPvX++zHBPBVAH/87HVdplDW/DGArxmGYQz6jrM02OYArIr/1zB44jv4fMEE8OeGYfzYMIzffvbalGmam8/+3gIw9XIuzcEIwm5uOHLGgRX+wbNQtn8uQqudueIAz0KR3gbwIRy54sAG2jwBHJniQINhGG7DMD4BsAPgWwAeASiYptl5doicD2quPHu/CCA96PxO0REHo4KfM03zHeyHnvyOYRi/IN809/tPOD0oHDwHZ244OAT/BMAl7IepbAL4v7/Uq3EwMjAMIwLgfwLwX5imWZLvOXLFAWExTxyZ4uA5mKbZNU3zLQDz2Pesvn6S5z9Lg20dwIL4f/7Zaw4cwDTN9We/dwD8a+xP9m2GnTz7vfPyrtDBiMFubjhyxkEfTNPcfraR9gD8UxyEKDlz5XMMwzC82FfC/wfTNP/Vs5cdueKgD1bzxJEpDgbBNM0CgP8I4EvYD5/2PHtLzgc1V569HweQG3TeszTYfgTg8rOKKT7sJ2b+2zP8fgcjCsMwwoZhRPk3gG8AuIX9+fGbzw77TQB/8nKu0MEIwm5u/FsA/9tnVd3eB1AUIU4OPofQco3+19iXLcD+XPk7z6p1XcB+QYkfnvX1OTh7PMsV+WcA7pim+d+Jtxy54kDBbp44MsWBDsMwJgzDSDz7Owjg69jPefyPAP72s8N0mUJZ87cB/IdnXn1beAa9eZIwTbNjGMY/APC/AHAD+OemaX52Vt/vYKQxBeBfP8u39AD4/5mm+e8Nw/gRgD8yDOO3ADwB8Osv8RodvCQYhvE/AvhFABnDMNYA/EMA/wjWc+NPAfx17Cd71wD87878gh28NNjMlV80DOMt7Ie3rQD43wOAaZqfGYbxRwBuY78a3O+Yptl9CZft4OzxZQB/D8DNZzknAPBfw5ErDvphN09+w5EpDjTMAPj9Z1VBXQD+yDTN/9kwjNsA/sAwjP8WwE+wTwDg2e//r2EYD7FfKOvvHPYFxiEGnQMHDhw4cODAgQMHDhw4eElwio44cODAgQMHDhw4cODAwYjCMdgcOHDgwIEDBw4cOHDgYEThGGwOHDhw4MCBAwcOHDhwMKJwDDYHDhw4cODAgQMHDhw4GFE4BpsDBw4cOHDgwIEDBw4cjCgcg82BAwcOHDhw4MCBAwcORhSOwebAgQMHDhw4cODAgQMHI4r/P/CEzv+gqWmyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = torchvision.utils.make_grid(images,nrow = 10)\n",
    "\n",
    "plt.figure(figsize = (15,15))\n",
    "plt.imshow(np.transpose(grid,(1,2,0)))\n",
    "\n",
    "print('labels',labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quiz03 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1:To see how many images are in our training set, we can check the length of the dataset   using the Python _______________ function.\n",
    "\n",
    "A1:len(train_set)\n",
    "\n",
    "Q2:Suppose we want to see the class for each image in a PyTorch Dataset. This can be done   by using which attribute?\n",
    "\n",
    "A2:train_set.targets\n",
    "\n",
    "Q3:Suppose we have the code below. The values inside the targets tensor are ___________\n",
    "  train_set.targets\n",
    "  tensor([9, 0, 0, ..., 3, 0, 5])\n",
    "  \n",
    "A3:encoded class names (labels)\n",
    "\n",
    "Q4:The Fashion-MNIST dataset is uniform with respect to the number of samples in each     class. This means we have the same number of samples for each class. As a result, this   dataset is said to be a _______________ dataset.\n",
    "\n",
    "A4:balanced\n",
    "\n",
    "Q5:If the classes in a dataset have a varying number of samples, the dataset is said to   be an _______________ dataset.\n",
    "\n",
    "A5:unbalanced"
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